In this script we conduct the estimation for the measure_arguments approach.

PROGRAMS=pg_arguments_full5_c200_opc15x2 SAMPLESIZE=50 NSAMPLES=1`.

Expected a result file besu_pg_arguments_full5_c200_opc15x2_50_1.csv.

# the programs file is too large to be placed in github
programs = read.csv(paste("../../local/", program_set_codename, ".csv", sep=""))

results = load_data_set(env, program_set_codename, measurement_codename)
# besu may have additional columns with gc stats
results = results[, c("program_id", "sample_id", "run_id", "measure_total_time_ns", "measure_total_timer_time_ns", "env")]
# TODO geth short-circuits zero length programs, resulting in zero timing somehow. Drop these more elegantly, not based on measure_total_time_ns
results = results[which(results$measure_total_time_ns != 0), ]

all_envs = c(env)
measurements = sqldf("SELECT opcode, op_count, arg0, arg1, arg2, sample_id, run_id, measure_total_time_ns, env, results.program_id
                     FROM results
                     INNER JOIN
                       programs ON(results.program_id = programs.program_id)
                     ")
measurements$opcode = factor(measurements$opcode, levels=unique(programs$opcode))
head(measurements)
##   opcode op_count arg0 arg1 arg2 sample_id run_id measure_total_time_ns  env
## 1    ADD        0   25   27   NA         0      0               3982569 besu
## 2    ADD        0   25   27   NA         0      1               5592032 besu
## 3    ADD        0   25   27   NA         0      2               8815079 besu
## 4    ADD        0   25   27   NA         0      3               5457919 besu
## 5    ADD        0   25   27   NA         0      4               5537141 besu
## 6    ADD        0   25   27   NA         0      5               5446211 besu
##   program_id
## 1      ADD_0
## 2      ADD_0
## 3      ADD_0
## 4      ADD_0
## 5      ADD_0
## 6      ADD_0

Remove outliers if needed.

# Extracts all OPCODEs from the `programs` data frame of the given arity (args taken off the stack).
extract_opcodes <- function(arity) {
  if (!missing(arity)) {
    if (arity == 0) {
      programs = programs[which(is.na(programs$arg0) & is.na(programs$arg1) & is.na(programs$arg2)), ]
    }
    if (arity == 1) {
      programs = programs[which(!is.na(programs$arg0) & is.na(programs$arg1) & is.na(programs$arg2)), ]
    }
    if (arity == 2) {
      programs = programs[which(!is.na(programs$arg1) & is.na(programs$arg2)), ]
    }
    if (arity == 3) {
      programs = programs[which(!is.na(programs$arg2)), ]
    }
  }
  unique(programs$opcode)
}
if ( (!removed_outliers) && (!removed_outliers_2)) {
  boxplot(measure_total_time_ns ~ opcode, data=measurements[which(measurements$env == env), ], las=2, outline=TRUE, log='y', main=paste(env, 'all'))
}

if (removed_outliers) {
  par(mfrow=c(length(all_envs)*2, 1))
  
  # before
  boxplot(measure_total_time_ns ~ opcode, data=measurements[which(measurements$env == env), ], las=2, outline=TRUE, log='y', main=paste(env, 'all'))

  measurements = remove_outliers(measurements, 'measure_total_time_ns', FALSE)
  
  # after
  boxplot(measure_total_time_ns ~ opcode, data=measurements[which(measurements$env == env), ], las=2, outline=TRUE, log='y', main=paste(env, 'no_outliers'))
}
all_opcodes = extract_opcodes()
nullary_opcodes = extract_opcodes(0)
unary_opcodes = extract_opcodes(1)
binary_opcodes = extract_opcodes(2)
ternary_opcodes = extract_opcodes(3)

div_opcodes = c('DIV', 'MOD', 'SDIV', 'SMOD')
measurements$expensive = NA
measurements[which(measurements$opcode %in% div_opcodes), ]$expensive =
  measurements[which(measurements$opcode %in% div_opcodes), ]$arg0 >
  measurements[which(measurements$opcode %in% div_opcodes), ]$arg1
# remember that argX is the byte-size of the argument in these measurements
measurements[which(measurements$opcode == 'ADDMOD'), ]$expensive =
  8**measurements[which(measurements$opcode == 'ADDMOD'), ]$arg0 +
  8**measurements[which(measurements$opcode == 'ADDMOD'), ]$arg1 > 
  8**measurements[which(measurements$opcode == 'ADDMOD'), ]$arg2
measurements[which(measurements$opcode == 'MULMOD'), ]$expensive =
  measurements[which(measurements$opcode == 'MULMOD'), ]$arg0 +
  measurements[which(measurements$opcode == 'MULMOD'), ]$arg1 >
  measurements[which(measurements$opcode == 'MULMOD'), ]$arg2
if (removed_outliers_2) {
  measurements = remove_compare_outliers(measurements, 'measure_total_time_ns', all_envs)
}

Detailed view

This is massive and detailed overview on the impact of arguments. Because of the number of charts, only op count = 30 is eligible. Feel free to change it, but that should not be anyhow more informative. The visualizations do not guarantee that all dependencies are clearly seen. Especially for binary and ternary opcodes where impacts of arg0, arg1 and arg2 are mixed. But if a dependency is graphically noticeable that you should expect also statistical dependency.

for (env in all_envs) {
  for (opcode in unary_opcodes) {
#    plot(measure_total_time_ns ~ arg0, data=measurements[which(measurements$env == env & measurements$opcode == opcode & measurements$op_count == 0), ], pch=0, col='darkgreen')
#    title(main = paste(env, opcode, 'arg0', 'opcount 0'))
#    plot(measure_total_time_ns ~ arg0, data=measurements[which(measurements$env == env & measurements$opcode == opcode & measurements$op_count == 15), ], pch=1, col='red')
#    title(main = paste(env, opcode, 'arg0', 'opcount 15'))
    plot(measure_total_time_ns ~ arg0, data=measurements[which(measurements$env == env & measurements$opcode == opcode & measurements$op_count == 30), ], pch=5, col='blue')
    title(main = paste(env, opcode, 'arg0', 'opcount 30'))
  } 
  for (opcode in binary_opcodes) {
#    plot(measure_total_time_ns ~ arg0, data=measurements[which(measurements$env == env & measurements$opcode == opcode & measurements$op_count == 0), ], pch=0, col='darkgreen')
#    title(main = paste(env, opcode, 'arg0', 'opcount 0'))
#    plot(measure_total_time_ns ~ arg0, data=measurements[which(measurements$env == env & measurements$opcode == opcode & measurements$op_count == 15), ], pch=1, col='red')
#    title(main = paste(env, opcode, 'arg0', 'opcount 15'))
    plot(measure_total_time_ns ~ arg0, data=measurements[which(measurements$env == env & measurements$opcode == opcode & measurements$op_count == 30), ], pch=5, col='blue')
    title(main = paste(env, opcode, 'arg0', 'opcount 30'))
#    plot(measure_total_time_ns ~ arg1, data=measurements[which(measurements$env == env & measurements$opcode == opcode & measurements$op_count == 0), ], pch=0, col='darkgreen')
#    title(main = paste(env, opcode, 'arg1', 'opcount 0'))
#    plot(measure_total_time_ns ~ arg1, data=measurements[which(measurements$env == env & measurements$opcode == opcode & measurements$op_count == 15), ], pch=1, col='red')
#    title(main = paste(env, opcode, 'arg1', 'opcount 15'))
    plot(measure_total_time_ns ~ arg1, data=measurements[which(measurements$env == env & measurements$opcode == opcode & measurements$op_count == 30), ], pch=5, col='blue')
    title(main = paste(env, opcode, 'arg1', 'opcount 30'))
  } 
  for (opcode in ternary_opcodes) {
#    plot(measure_total_time_ns ~ arg0, data=measurements[which(measurements$env == env & measurements$opcode == opcode & measurements$op_count == 0), ], pch=0, col='darkgreen')
#    title(main = paste(env, opcode, 'arg0', 'opcount 0'))
#    plot(measure_total_time_ns ~ arg0, data=measurements[which(measurements$env == env & measurements$opcode == opcode & measurements$op_count == 15), ], pch=1, col='red')
#    title(main = paste(env, opcode, 'arg0', 'opcount 15'))
    plot(measure_total_time_ns ~ arg0, data=measurements[which(measurements$env == env & measurements$opcode == opcode & measurements$op_count == 30), ], pch=5, col='blue')
    title(main = paste(env, opcode, 'arg0', 'opcount 30'))
#    plot(measure_total_time_ns ~ arg1, data=measurements[which(measurements$env == env & measurements$opcode == opcode & measurements$op_count == 0), ], pch=0, col='darkgreen')
#    title(main = paste(env, opcode, 'arg1', 'opcount 0'))
#    plot(measure_total_time_ns ~ arg1, data=measurements[which(measurements$env == env & measurements$opcode == opcode & measurements$op_count == 15), ], pch=1, col='red')
#    title(main = paste(env, opcode, 'arg1', 'opcount 15'))
    plot(measure_total_time_ns ~ arg1, data=measurements[which(measurements$env == env & measurements$opcode == opcode & measurements$op_count == 30), ], pch=5, col='blue')
    title(main = paste(env, opcode, 'arg1', 'opcount 30'))
#    plot(measure_total_time_ns ~ arg2, data=measurements[which(measurements$env == env & measurements$opcode == opcode & measurements$op_count == 0), ], pch=0, col='darkgreen')
#    title(main = paste(env, opcode, 'arg2', 'opcount 0'))
#    plot(measure_total_time_ns ~ arg2, data=measurements[which(measurements$env == env & measurements$opcode == opcode & measurements$op_count == 15), ], pch=1, col='red')
#    title(main = paste(env, opcode, 'arg2', 'opcount 15'))
    plot(measure_total_time_ns ~ arg2, data=measurements[which(measurements$env == env & measurements$opcode == opcode & measurements$op_count == 30), ], pch=5, col='blue')
    title(main = paste(env, opcode, 'arg2', 'opcount 30'))
  } 
}

Models

Notes: 1. Outliers need to be removed if detected 2. The argX:op_count interactions measure the impact on the OPCODE 3. The argX are just auxiliary variables added to exclude the effect of cheaper/more expensive PUSHes. We only want to extract the effect of the argument on the measured OPCODE repeated op_count times.

# Every `arg` coefficient represents the impact of the argument's byte size growing by 1.
# We treat as impactful the arguments where p-value is effectively zero. The previous approach was:
# Treat as impactful the arguments, where:
# 1. The estimate is significant with confidence 0.001
# 2. The increase of arg's byte size by 1 will increase the cost by more than 1%
# but it turned out to be much less stable in practice.
p_value_thresh = 1e-30
# p_value_thresh = 0.001
impact_ratio = 0.00
# impact_ratio = 0.01

arg_lm <- function(df, opcode, env, formula) {
  data = df[which(df$opcode==opcode & df$env==env), ]
  lm(formula, data=data)
}

# Adds the results from the estimated `model` to the `results_df` data frame.
# You need to provide the corresponding `opcode`, `env` and `arity`.
# `results_df` is assumed to have the columns as the `first_pass` data frame has (see below)
add_arg_results <- function(model, opcode, env, results_df, arity) {
  stopifnot(arity > 0)

  all_coefficients = summary(model)$coefficients
  arg_coefficients = all_coefficients[!(row.names(all_coefficients) %in% c("op_count", "(Intercept)", "arg0", "arg1", "arg2")),]
  pure_op_count_coeff = all_coefficients["op_count", 1]
  # will be filled if any is impacting
  args_ns = c(NA, NA, NA)
  # will be always if arg present
  args_ns_raw = c(NA, NA, NA)
  args_ns_p = c(NA, NA, NA)

  if (arity == 1) {
    # there's only one arg coefficient here, silly R forces us to take a special case path...
    has_significant = arg_coefficients[4] < p_value_thresh
  
    if (has_significant) {
      coefficient_impact = abs(arg_coefficients[1])
      has_impacting = has_significant & coefficient_impact > pure_op_count_coeff * impact_ratio
    } else {
      has_impacting = FALSE
    }
    if (has_impacting) {
      args_ns[1] = arg_coefficients[1]
    }
    args_ns_raw[1] = arg_coefficients[1]
    args_ns_p[1] = arg_coefficients[4]
  } else {
    significant = arg_coefficients[, 4] < p_value_thresh
    has_significant = length(which(significant)) > 0
  
    coefficient_impact = abs(arg_coefficients[, 1])
    can_impact = significant & coefficient_impact > pure_op_count_coeff * impact_ratio
    has_impacting = length(which(can_impact)) > 0
    args_ns[which(can_impact)] = arg_coefficients[which(can_impact), 1]
    args_ns_raw[1:arity] = arg_coefficients[1:arity, 1]
    args_ns_p[1:arity] = arg_coefficients[1:arity, 4]
  }
  
  # NAs for the "expensive" arg columns. See above for the columns layout
  results_df[nrow(results_df) + 1, ] = c(opcode, env, has_significant, has_impacting, pure_op_count_coeff, args_ns, NA, args_ns_raw, NA, args_ns_p, NA)
  return(results_df)
}

# Adds the results from the estimated `model` to the `results_df` data frame, where the model is
# specifically the one gauged towards the "division" OPCODEs like `DIV`.
# See also `add_arg_results`
add_arg_expensive_results <- function(model, opcode, env, results_df, arity) {
  stopifnot(arity > 0)

  all_coefficients = summary(model)$coefficients
  pure_op_count_coeff = all_coefficients["op_count", 1]
  expensive = NA
  
  # there's only one arg coefficient here, silly R forces us to take a special case path...
  has_significant = all_coefficients['op_count:expensiveTRUE', 4] < p_value_thresh

  if (has_significant) {
    coefficient_impact = abs(all_coefficients['op_count:expensiveTRUE', 1])
    has_impacting = has_significant & coefficient_impact > pure_op_count_coeff * impact_ratio
  } else {
    has_impacting = FALSE
  }
  if (has_impacting) {
    expensive = all_coefficients['op_count:expensiveTRUE', 1]
  }
  expensive_raw = all_coefficients['op_count:expensiveTRUE', 1]
  expensive_p = all_coefficients['op_count:expensiveTRUE', 4]
  results_df[which(results_df$opcode == opcode & results_df$env == env), 'expensive_ns'] = expensive
  results_df[which(results_df$opcode == opcode & results_df$env == env), 'expensive_ns_raw'] = expensive_raw
  results_df[which(results_df$opcode == opcode & results_df$env == env), 'expensive_ns_p'] = expensive_p
  return(results_df)
}

# Goes through all the families of OPCODEs and fits and displays their respective `measure_arguments`
# models.
# Results are gathered in a common `results_df` data frame.
analyze_for_env <- function(df, results_df, env) {
  for (opcode in unary_opcodes) {
    model = arg_lm(df, opcode, env, measure_total_time_ns ~ op_count + arg0 + arg0:op_count)
    print(c(opcode, env))
    print(summary(model))
    results_df = add_arg_results(model, opcode, env, results_df, 1)
  }
  for (opcode in binary_opcodes) {
    model = arg_lm(df, opcode, env, measure_total_time_ns ~ op_count + arg0 + arg1 + arg0:op_count + arg1:op_count)
    print(c(opcode, env))
    print(summary(model))
    results_df = add_arg_results(model, opcode, env, results_df, 2)
  }
  for (opcode in ternary_opcodes) {
    model = arg_lm(df, opcode, env, measure_total_time_ns ~ op_count + arg0 + arg1 + arg2 + arg0:op_count + arg1:op_count + arg2:op_count)
    print(c(opcode, env))
    print(summary(model))
    results_df = add_arg_results(model, opcode, env, results_df, 3)
  }
  for (opcode in div_opcodes) {
    model = arg_lm(df, opcode, env, measure_total_time_ns ~ op_count + arg0 + arg1 + expensive:op_count)
    print(c(opcode, env))
    print(summary(model))
    results_df = add_arg_expensive_results(model, opcode, env, results_df, 2)
  }
  for (opcode in c('ADDMOD', 'MULMOD')) {
    model = arg_lm(df, opcode, env, measure_total_time_ns ~ op_count + arg0 + arg1 + arg2 + expensive:op_count)
    print(c(opcode, env))
    print(summary(model))
    results_df = add_arg_expensive_results(model, opcode, env, results_df, 3)
  }
  return(results_df)
}

This is the so-called “first-pass” at the estimation procedure, where we estimated all possible argument impact variables for all OPCODEs. We gather all the results in the first_pass table, inspect this to see where the arguments turned out to be significantly impacting the computation cost.

first_pass = data.frame(matrix(ncol = 17, nrow = 0))
colnames(first_pass) <- c('opcode', 'env', 'has_significant', 'has_impacting', 'estimate_marginal_ns',
                          'arg0_ns', 'arg1_ns', 'arg2_ns', 'expensive_ns',
                          'arg0_ns_raw', 'arg1_ns_raw', 'arg2_ns_raw', 'expensive_ns_raw',
                          'arg0_ns_p', 'arg1_ns_p', 'arg2_ns_p',  'expensive_ns_p')

first_pass = analyze_for_env(measurements, first_pass, env)
## [1] "ISZERO" "besu"  
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1060457  -847419  -728366   995771 14083973 
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)   4132643.82   22620.57 182.694 <0.0000000000000002 ***
## op_count        31999.46    1168.12  27.394 <0.0000000000000002 ***
## arg0              682.32    1265.21   0.539               0.590    
## op_count:arg0     -40.82      65.34  -0.625               0.532    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1248000 on 29996 degrees of freedom
## Multiple R-squared:  0.08656,    Adjusted R-squared:  0.08647 
## F-statistic: 947.5 on 3 and 29996 DF,  p-value: < 0.00000000000000022
## 
## [1] "NOT"  "besu"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1107185  -863900  -736841  1125167 12084693 
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)   4141164.99   26148.33 158.372 <0.0000000000000002 ***
## op_count        35303.23    1350.29  26.145 <0.0000000000000002 ***
## arg0              -82.08    1346.81  -0.061               0.951    
## op_count:arg0     -24.29      69.55  -0.349               0.727    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1343000 on 29996 degrees of freedom
## Multiple R-squared:  0.09198,    Adjusted R-squared:  0.09189 
## F-statistic:  1013 on 3 and 29996 DF,  p-value: < 0.00000000000000022
## 
## [1] "CALLDATALOAD" "besu"        
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1519415 -1273183 -1205196 -1047283 27989872 
## 
## Coefficients:
##                   Estimate   Std. Error t value            Pr(>|t|)    
## (Intercept)   6445387.0724   54652.5448 117.934 <0.0000000000000002 ***
## op_count        58894.5102    2822.2453  20.868 <0.0000000000000002 ***
## arg0                2.1622       5.4544   0.396               0.692    
## op_count:arg0       0.1082       0.2817   0.384               0.701    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2872000 on 29996 degrees of freedom
## Multiple R-squared:  0.0612, Adjusted R-squared:  0.06111 
## F-statistic: 651.8 on 3 and 29996 DF,  p-value: < 0.00000000000000022
## 
## [1] "POP"  "besu"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1003736  -567559  -474536   598786  5523553 
## 
## Coefficients:
##                 Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)   2314221.38   15615.19 148.203 < 0.0000000000000002 ***
## op_count        23567.67     806.36  29.227 < 0.0000000000000002 ***
## arg0             5041.70     816.08   6.178       0.000000000658 ***
## op_count:arg0    -142.34      42.14  -3.378             0.000732 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 837400 on 29996 degrees of freedom
## Multiple R-squared:  0.08886,    Adjusted R-squared:  0.08877 
## F-statistic: 975.2 on 3 and 29996 DF,  p-value: < 0.00000000000000022
## 
## [1] "MLOAD" "besu" 
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1387736 -1185657 -1129417  -949333 22813147 
## 
## Coefficients:
##                   Estimate   Std. Error t value            Pr(>|t|)    
## (Intercept)   6397229.8138   53553.2921 119.455 <0.0000000000000002 ***
## op_count        26236.4589    2765.4801   9.487 <0.0000000000000002 ***
## arg0                0.5826       5.2323   0.111               0.911    
## op_count:arg0      -0.1532       0.2702  -0.567               0.571    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2725000 on 29996 degrees of freedom
## Multiple R-squared:  0.01234,    Adjusted R-squared:  0.01224 
## F-statistic: 124.9 on 3 and 29996 DF,  p-value: < 0.00000000000000022
## 
## [1] "JUMPI" "besu" 
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
##  -916939  -754302  -704140   890577 11450723 
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)   4073354.04   20021.22 203.452 <0.0000000000000002 ***
## op_count        19246.02    1033.89  18.615 <0.0000000000000002 ***
## arg0              591.77    1079.27   0.548               0.583    
## op_count:arg0     -11.77      55.73  -0.211               0.833    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1122000 on 29996 degrees of freedom
## Multiple R-squared:  0.04151,    Adjusted R-squared:  0.04141 
## F-statistic:   433 on 3 and 29996 DF,  p-value: < 0.00000000000000022
## 
## [1] "DUP1" "besu"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -831138 -712741 -665680  799763 6836287 
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)   4121194.79   20456.61 201.460 <0.0000000000000002 ***
## op_count        10321.24    1056.37   9.770 <0.0000000000000002 ***
## arg0              236.53    1059.40   0.223               0.823    
## op_count:arg0     -37.27      54.71  -0.681               0.496    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1069000 on 29996 degrees of freedom
## Multiple R-squared:  0.01219,    Adjusted R-squared:  0.01209 
## F-statistic: 123.4 on 3 and 29996 DF,  p-value: < 0.00000000000000022
## 
## [1] "DUP2" "besu"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
##  -834486  -709984  -665000   803518 14766076 
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)   4134314.98   18924.12 218.468 <0.0000000000000002 ***
## op_count         9695.05     977.24   9.921 <0.0000000000000002 ***
## arg0             -934.83    1004.98  -0.930               0.352    
## op_count:arg0      22.11      51.90   0.426               0.670    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1061000 on 29996 degrees of freedom
## Multiple R-squared:  0.01331,    Adjusted R-squared:  0.01321 
## F-statistic: 134.9 on 3 and 29996 DF,  p-value: < 0.00000000000000022
## 
## [1] "DUP3" "besu"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -836674 -716168 -670148  791802 6627959 
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)   4137820.05   20428.21 202.554 <0.0000000000000002 ***
## op_count         9524.06    1054.91   9.028 <0.0000000000000002 ***
## arg0             -659.03    1111.32  -0.593               0.553    
## op_count:arg0      19.14      57.39   0.334               0.739    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1085000 on 29996 degrees of freedom
## Multiple R-squared:  0.01217,    Adjusted R-squared:  0.01207 
## F-statistic: 123.2 on 3 and 29996 DF,  p-value: < 0.00000000000000022
## 
## [1] "DUP4" "besu"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -841897 -721093 -674366  791160 9671516 
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)   4146867.81   20163.27 205.664 <0.0000000000000002 ***
## op_count         8979.22    1041.23   8.624 <0.0000000000000002 ***
## arg0             -568.07    1064.41  -0.534               0.594    
## op_count:arg0      28.84      54.97   0.525               0.600    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1090000 on 29996 degrees of freedom
## Multiple R-squared:  0.01117,    Adjusted R-squared:  0.01107 
## F-statistic: 112.9 on 3 and 29996 DF,  p-value: < 0.00000000000000022
## 
## [1] "DUP5" "besu"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -828700 -709724 -662940  803383 6700240 
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)   4128791.00   19388.24 212.953 <0.0000000000000002 ***
## op_count        10060.78    1001.20  10.049 <0.0000000000000002 ***
## arg0             -399.26    1007.73  -0.396               0.692    
## op_count:arg0     -16.94      52.04  -0.325               0.745    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1060000 on 29996 degrees of freedom
## Multiple R-squared:  0.01263,    Adjusted R-squared:  0.01254 
## F-statistic: 127.9 on 3 and 29996 DF,  p-value: < 0.00000000000000022
## 
## [1] "DUP6" "besu"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -831530 -716514 -670310  798397 6700253 
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)   4119078.61   21212.38 194.183 <0.0000000000000002 ***
## op_count        10187.46    1095.40   9.300 <0.0000000000000002 ***
## arg0              233.68    1102.01   0.212               0.832    
## op_count:arg0     -16.20      56.91  -0.285               0.776    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1075000 on 29996 degrees of freedom
## Multiple R-squared:  0.01259,    Adjusted R-squared:  0.01249 
## F-statistic: 127.5 on 3 and 29996 DF,  p-value: < 0.00000000000000022
## 
## [1] "DUP7" "besu"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -837408 -719899 -672398  804233 6873987 
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)   4162202.82   20444.19 203.589 <0.0000000000000002 ***
## op_count         9391.47    1055.73   8.896 <0.0000000000000002 ***
## arg0             -227.34    1095.83  -0.207               0.836    
## op_count:arg0      11.26      56.59   0.199               0.842    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1068000 on 29996 degrees of freedom
## Multiple R-squared:  0.01193,    Adjusted R-squared:  0.01183 
## F-statistic: 120.7 on 3 and 29996 DF,  p-value: < 0.00000000000000022
## 
## [1] "DUP8" "besu"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -865412 -734168 -686372  801428 7207796 
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)   4186065.38   19728.84 212.180 <0.0000000000000002 ***
## op_count         9890.84    1018.79   9.708 <0.0000000000000002 ***
## arg0             -267.88    1123.45  -0.238               0.812    
## op_count:arg0      37.77      58.01   0.651               0.515    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1096000 on 29996 degrees of freedom
## Multiple R-squared:  0.0135, Adjusted R-squared:  0.01341 
## F-statistic: 136.9 on 3 and 29996 DF,  p-value: < 0.00000000000000022
## 
## [1] "DUP9" "besu"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
##  -844309  -720682  -674027   794711 13099809 
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)   4105969.26   20556.09 199.745 <0.0000000000000002 ***
## op_count        11202.93    1061.51  10.554 <0.0000000000000002 ***
## arg0             1341.01    1120.43   1.197               0.231    
## op_count:arg0     -71.46      57.86  -1.235               0.217    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1089000 on 29996 degrees of freedom
## Multiple R-squared:  0.01269,    Adjusted R-squared:  0.01259 
## F-statistic: 128.5 on 3 and 29996 DF,  p-value: < 0.00000000000000022
## 
## [1] "DUP10" "besu" 
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -830714 -714547 -668248  796378 6301492 
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)   4129045.30   20600.25 200.437 <0.0000000000000002 ***
## op_count         9059.65    1063.79   8.516 <0.0000000000000002 ***
## arg0             -279.99    1027.37  -0.273               0.785    
## op_count:arg0      53.00      53.05   0.999               0.318    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1075000 on 29996 degrees of freedom
## Multiple R-squared:  0.01285,    Adjusted R-squared:  0.01275 
## F-statistic: 130.2 on 3 and 29996 DF,  p-value: < 0.00000000000000022
## 
## [1] "DUP11" "besu" 
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -875129 -744075 -692387  810016 6846893 
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)   4221536.23   21004.84 200.979 <0.0000000000000002 ***
## op_count        10914.53    1084.69  10.062 <0.0000000000000002 ***
## arg0              471.18    1098.98   0.429               0.668    
## op_count:arg0     -38.96      56.75  -0.687               0.492    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1088000 on 29996 degrees of freedom
## Multiple R-squared:  0.01317,    Adjusted R-squared:  0.01307 
## F-statistic: 133.4 on 3 and 29996 DF,  p-value: < 0.00000000000000022
## 
## [1] "DUP12" "besu" 
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
##  -843361  -721669  -674859   791035 16949621 
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)   4128958.77   19876.28 207.733 <0.0000000000000002 ***
## op_count         9817.15    1026.41   9.565 <0.0000000000000002 ***
## arg0              551.72    1054.53   0.523               0.601    
## op_count:arg0     -17.65      54.46  -0.324               0.746    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1092000 on 29996 degrees of freedom
## Multiple R-squared:  0.0113, Adjusted R-squared:  0.0112 
## F-statistic: 114.3 on 3 and 29996 DF,  p-value: < 0.00000000000000022
## 
## [1] "DUP13" "besu" 
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
##  -845742  -719683  -672757   803951 11132078 
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)   4163493.16   20759.14 200.562 <0.0000000000000002 ***
## op_count         8887.76    1072.00   8.291 <0.0000000000000002 ***
## arg0             -305.92    1064.98  -0.287               0.774    
## op_count:arg0      36.56      55.00   0.665               0.506    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1060000 on 29996 degrees of freedom
## Multiple R-squared:  0.01197,    Adjusted R-squared:  0.01187 
## F-statistic: 121.1 on 3 and 29996 DF,  p-value: < 0.00000000000000022
## 
## [1] "DUP14" "besu" 
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
##  -844987  -722990  -675816   803002 16302795 
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)   4154979.90   20047.44 207.257 <0.0000000000000002 ***
## op_count         9671.91    1035.25   9.343 <0.0000000000000002 ***
## arg0              -77.37    1079.83  -0.072               0.943    
## op_count:arg0      14.08      55.76   0.253               0.801    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1079000 on 29996 degrees of freedom
## Multiple R-squared:  0.01247,    Adjusted R-squared:  0.01238 
## F-statistic: 126.3 on 3 and 29996 DF,  p-value: < 0.00000000000000022
## 
## [1] "DUP15" "besu" 
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -835286 -713295 -666919  799078 9016056 
## 
## Coefficients:
##                  Estimate  Std. Error t value            Pr(>|t|)    
## (Intercept)   4123734.765   22060.000 186.933 <0.0000000000000002 ***
## op_count        10058.845    1139.174   8.830 <0.0000000000000002 ***
## arg0              -96.024    1141.306  -0.084               0.933    
## op_count:arg0      -0.898      58.937  -0.015               0.988    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1073000 on 29996 degrees of freedom
## Multiple R-squared:  0.01298,    Adjusted R-squared:  0.01288 
## F-statistic: 131.5 on 3 and 29996 DF,  p-value: < 0.00000000000000022
## 
## [1] "DUP16" "besu" 
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
##  -911101  -770606  -713074   813799 12491745 
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)   4310094.16   21428.76 201.136 <0.0000000000000002 ***
## op_count        11105.95    1106.58  10.036 <0.0000000000000002 ***
## arg0              160.55    1118.63   0.144               0.886    
## op_count:arg0     -33.66      57.77  -0.583               0.560    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1129000 on 29996 degrees of freedom
## Multiple R-squared:  0.01293,    Adjusted R-squared:  0.01283 
## F-statistic: 130.9 on 3 and 29996 DF,  p-value: < 0.00000000000000022
## 
## [1] "ADD"  "besu"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1452551  -971335  -772328   861188 17081974 
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)   4141012.16   40336.52 102.662 <0.0000000000000002 ***
## op_count        59139.74    2082.97  28.392 <0.0000000000000002 ***
## arg0             1286.96    1589.48   0.810               0.418    
## arg1             -115.45    1553.61  -0.074               0.941    
## op_count:arg0     995.35      82.08  12.126 <0.0000000000000002 ***
## op_count:arg1    1029.12      80.23  12.827 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1569000 on 29994 degrees of freedom
## Multiple R-squared:  0.3587, Adjusted R-squared:  0.3586 
## F-statistic:  3356 on 5 and 29994 DF,  p-value: < 0.00000000000000022
## 
## [1] "MUL"  "besu"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1333931  -968576  -749667   822688 18338302 
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)   4120930.48   40757.93 101.107 <0.0000000000000002 ***
## op_count        92060.50    2104.73  43.740 <0.0000000000000002 ***
## arg0              300.45    1611.41   0.186               0.852    
## arg1              740.15    1614.92   0.458               0.647    
## op_count:arg0     -40.53      83.21  -0.487               0.626    
## op_count:arg1     -58.86      83.39  -0.706               0.480    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1581000 on 29994 degrees of freedom
## Multiple R-squared:  0.3296, Adjusted R-squared:  0.3295 
## F-statistic:  2949 on 5 and 29994 DF,  p-value: < 0.00000000000000022
## 
## [1] "SUB"  "besu"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1245478  -941385  -762172   883249  9947946 
## 
## Coefficients:
##                  Estimate  Std. Error t value            Pr(>|t|)    
## (Intercept)   4138407.404   38685.317 106.976 <0.0000000000000002 ***
## op_count        58148.108    1997.701  29.108 <0.0000000000000002 ***
## arg0              714.573    1526.006   0.468               0.640    
## arg1              241.855    1518.447   0.159               0.873    
## op_count:arg0     -69.634      78.803  -0.884               0.377    
## op_count:arg1       4.659      78.412   0.059               0.953    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1491000 on 29994 degrees of freedom
## Multiple R-squared:  0.1803, Adjusted R-squared:  0.1801 
## F-statistic:  1319 on 5 and 29994 DF,  p-value: < 0.00000000000000022
## 
## [1] "DIV"  "besu"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2405845  -907432  -710704   813907 15550762 
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)   4167055.49   41184.45 101.180 <0.0000000000000002 ***
## op_count        64440.81    2126.76  30.300 <0.0000000000000002 ***
## arg0              638.58    1648.54   0.387               0.698    
## arg1            -1170.95    1611.29  -0.727               0.467    
## op_count:arg0    3280.95      85.13  38.540 <0.0000000000000002 ***
## op_count:arg1    -886.72      83.21 -10.657 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1659000 on 29994 degrees of freedom
## Multiple R-squared:  0.4203, Adjusted R-squared:  0.4202 
## F-statistic:  4350 on 5 and 29994 DF,  p-value: < 0.00000000000000022
## 
## [1] "SDIV" "besu"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -3174675 -1094038  -818233   582064 18761620 
## 
## Coefficients:
##                Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)   4243653.4    53764.6  78.930 <0.0000000000000002 ***
## op_count       117636.6     2776.4  42.370 <0.0000000000000002 ***
## arg0              820.1     2186.7   0.375               0.708    
## arg1              118.6     2197.9   0.054               0.957    
## op_count:arg0    3813.5      112.9  33.771 <0.0000000000000002 ***
## op_count:arg1   -1002.9      113.5  -8.836 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2216000 on 29994 degrees of freedom
## Multiple R-squared:  0.4829, Adjusted R-squared:  0.4828 
## F-statistic:  5602 on 5 and 29994 DF,  p-value: < 0.00000000000000022
## 
## [1] "MOD"  "besu"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -3686767 -1039670  -793017   583610 22307878 
## 
## Coefficients:
##                Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)   4262321.3    55641.6  76.603 < 0.0000000000000002 ***
## op_count       106716.4     2873.3  37.140 < 0.0000000000000002 ***
## arg0             1832.5     2179.9   0.841                0.401    
## arg1            -1577.9     2198.9  -0.718                0.473    
## op_count:arg0    4043.6      112.6  35.922 < 0.0000000000000002 ***
## op_count:arg1    -612.9      113.6  -5.397         0.0000000682 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2212000 on 29994 degrees of freedom
## Multiple R-squared:  0.4935, Adjusted R-squared:  0.4934 
## F-statistic:  5845 on 5 and 29994 DF,  p-value: < 0.00000000000000022
## 
## [1] "SMOD" "besu"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -3570383 -1055564  -810531   594719 21869282 
## 
## Coefficients:
##                Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)   4272575.5    55135.1  77.493 < 0.0000000000000002 ***
## op_count       107143.6     2847.2  37.632 < 0.0000000000000002 ***
## arg0              329.8     2118.7   0.156                0.876    
## arg1             -828.4     2309.5  -0.359                0.720    
## op_count:arg0    4144.2      109.4  37.878 < 0.0000000000000002 ***
## op_count:arg1    -622.3      119.3  -5.218          0.000000182 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2198000 on 29994 degrees of freedom
## Multiple R-squared:  0.4882, Adjusted R-squared:  0.4881 
## F-statistic:  5722 on 5 and 29994 DF,  p-value: < 0.00000000000000022
## 
## [1] "EXP"  "besu"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##        Min         1Q     Median         3Q        Max 
## -147840350  -29634522    -807198    5925872  234791770 
## 
## Coefficients:
##               Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)    4282477    1394834   3.070              0.00214 ** 
## op_count        -65006      72029  -0.902              0.36680    
## arg0             -1452      59364  -0.024              0.98049    
## arg1             -1514      57302  -0.026              0.97891    
## op_count:arg0    27402       3066   8.939 < 0.0000000000000002 ***
## op_count:arg1   161222       2959  54.484 < 0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 61210000 on 29994 degrees of freedom
## Multiple R-squared:  0.3828, Adjusted R-squared:  0.3827 
## F-statistic:  3721 on 5 and 29994 DF,  p-value: < 0.00000000000000022
## 
## [1] "SIGNEXTEND" "besu"      
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1311586  -970724  -759764   821274 16474463 
## 
## Coefficients:
##                  Estimate  Std. Error t value            Pr(>|t|)    
## (Intercept)   4148547.139   37895.804 109.472 <0.0000000000000002 ***
## op_count       146350.280    1956.931  74.786 <0.0000000000000002 ***
## arg0               -1.469    1524.030  -0.001               0.999    
## arg1             -135.033    1572.033  -0.086               0.932    
## op_count:arg0     -22.153      78.701  -0.281               0.778    
## op_count:arg1     -20.448      81.179  -0.252               0.801    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1564000 on 29994 degrees of freedom
## Multiple R-squared:  0.5656, Adjusted R-squared:  0.5655 
## F-statistic:  7809 on 5 and 29994 DF,  p-value: < 0.00000000000000022
## 
## [1] "LT"   "besu"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1180063  -906631  -750395  1018892 11134871 
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)   4132145.01   35910.27 115.069 <0.0000000000000002 ***
## op_count        55058.75    1854.40  29.691 <0.0000000000000002 ***
## arg0             1093.68    1417.68   0.771               0.440    
## arg1             -279.39    1458.26  -0.192               0.848    
## op_count:arg0    -103.76      73.21  -1.417               0.156    
## op_count:arg1     -28.15      75.30  -0.374               0.709    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1399000 on 29994 degrees of freedom
## Multiple R-squared:  0.1768, Adjusted R-squared:  0.1766 
## F-statistic:  1288 on 5 and 29994 DF,  p-value: < 0.00000000000000022
## 
## [1] "GT"   "besu"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1197805  -916813  -756633  1016013  8736174 
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)   4154932.45   35580.53 116.775 <0.0000000000000002 ***
## op_count        53809.41    1837.37  29.286 <0.0000000000000002 ***
## arg0             -739.19    1494.58  -0.495               0.621    
## arg1              726.71    1446.54   0.502               0.615    
## op_count:arg0      24.55      77.18   0.318               0.750    
## op_count:arg1     -57.58      74.70  -0.771               0.441    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1427000 on 29994 degrees of freedom
## Multiple R-squared:  0.1728, Adjusted R-squared:  0.1727 
## F-statistic:  1253 on 5 and 29994 DF,  p-value: < 0.00000000000000022
## 
## [1] "SLT"  "besu"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1167875  -863149  -734138  1099464 11260398 
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)   4129074.26   35835.98 115.221 <0.0000000000000002 ***
## op_count        43845.61    1850.56  23.693 <0.0000000000000002 ***
## arg0              639.29    1342.97   0.476               0.634    
## arg1              -22.96    1293.67  -0.018               0.986    
## op_count:arg0     654.23      69.35   9.434 <0.0000000000000002 ***
## op_count:arg1     746.94      66.80  11.181 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1333000 on 29994 degrees of freedom
## Multiple R-squared:  0.2876, Adjusted R-squared:  0.2875 
## F-statistic:  2422 on 5 and 29994 DF,  p-value: < 0.00000000000000022
## 
## [1] "SGT"  "besu"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1170274  -872378  -731210  1103054 15589137 
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)   4120257.05   33831.54 121.787 <0.0000000000000002 ***
## op_count        43797.41    1747.05  25.069 <0.0000000000000002 ***
## arg0              291.62    1442.04   0.202               0.840    
## arg1              561.18    1304.62   0.430               0.667    
## op_count:arg0     696.03      74.47   9.347 <0.0000000000000002 ***
## op_count:arg1     723.25      67.37  10.735 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1337000 on 29994 degrees of freedom
## Multiple R-squared:  0.2823, Adjusted R-squared:  0.2821 
## F-statistic:  2359 on 5 and 29994 DF,  p-value: < 0.00000000000000022
## 
## [1] "EQ"   "besu"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1208793  -909296  -753908  1013924 18396553 
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)   4146156.55   34852.13 118.964 <0.0000000000000002 ***
## op_count        53196.24    1799.76  29.557 <0.0000000000000002 ***
## arg0              755.33    1389.58   0.544               0.587    
## arg1             -389.53    1459.14  -0.267               0.790    
## op_count:arg0     -68.38      71.76  -0.953               0.341    
## op_count:arg1     -14.27      75.35  -0.189               0.850    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1418000 on 29994 degrees of freedom
## Multiple R-squared:  0.1674, Adjusted R-squared:  0.1673 
## F-statistic:  1206 on 5 and 29994 DF,  p-value: < 0.00000000000000022
## 
## [1] "AND"  "besu"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1233689  -937149  -751375   890441 12363117 
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)   4127451.23   36026.89 114.566 <0.0000000000000002 ***
## op_count        57094.48    1860.42  30.689 <0.0000000000000002 ***
## arg0               80.21    1427.16   0.056               0.955    
## arg1              915.59    1551.61   0.590               0.555    
## op_count:arg0     -10.77      73.70  -0.146               0.884    
## op_count:arg1     -55.36      80.13  -0.691               0.490    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1489000 on 29994 degrees of freedom
## Multiple R-squared:  0.175,  Adjusted R-squared:  0.1748 
## F-statistic:  1272 on 5 and 29994 DF,  p-value: < 0.00000000000000022
## 
## [1] "OR"   "besu"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1238620  -944064  -768957   895445 18273658 
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)   4160905.18   37291.85 111.577 <0.0000000000000002 ***
## op_count        55973.95    1925.74  29.066 <0.0000000000000002 ***
## arg0             -304.64    1503.49  -0.203               0.839    
## arg1              520.70    1501.03   0.347               0.729    
## op_count:arg0      12.00      77.64   0.155               0.877    
## op_count:arg1     -62.06      77.51  -0.801               0.423    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1492000 on 29994 degrees of freedom
## Multiple R-squared:  0.1702, Adjusted R-squared:   0.17 
## F-statistic:  1230 on 5 and 29994 DF,  p-value: < 0.00000000000000022
## 
## [1] "XOR"  "besu"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1241768  -939288  -755388   876842  9928134 
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)   4144817.48   36156.82 114.634 <0.0000000000000002 ***
## op_count        56799.39    1867.13  30.421 <0.0000000000000002 ***
## arg0             -118.81    1435.37  -0.083               0.934    
## arg1              396.28    1450.50   0.273               0.785    
## op_count:arg0     -11.17      74.12  -0.151               0.880    
## op_count:arg1     -50.82      74.90  -0.679               0.497    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1492000 on 29994 degrees of freedom
## Multiple R-squared:  0.1732, Adjusted R-squared:  0.1731 
## F-statistic:  1257 on 5 and 29994 DF,  p-value: < 0.00000000000000022
## 
## [1] "BYTE" "besu"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1222118  -917128  -749720  1086687  9992078 
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)   4145068.99   36882.81 112.385 <0.0000000000000002 ***
## op_count        52660.87    1904.62  27.649 <0.0000000000000002 ***
## arg0             -442.59    1461.44  -0.303               0.762    
## arg1              719.36    1434.63   0.501               0.616    
## op_count:arg0     -79.43      75.47  -1.052               0.293    
## op_count:arg1      47.30      74.08   0.639               0.523    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1416000 on 29994 degrees of freedom
## Multiple R-squared:  0.169,  Adjusted R-squared:  0.1689 
## F-statistic:  1220 on 5 and 29994 DF,  p-value: < 0.00000000000000022
## 
## [1] "SHL"  "besu"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1027549  -750902  -681411   844822 10995282 
## 
## Coefficients:
##                 Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)   4155756.77   27337.96 152.014 < 0.0000000000000002 ***
## op_count        28189.52    1411.73  19.968 < 0.0000000000000002 ***
## arg0            -1492.03    1079.01  -1.383                0.167    
## arg1               83.91    1121.58   0.075                0.940    
## op_count:arg0    -397.66      55.72  -7.137    0.000000000000977 ***
## op_count:arg1      24.22      57.92   0.418                0.676    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1114000 on 29994 degrees of freedom
## Multiple R-squared:  0.06032,    Adjusted R-squared:  0.06016 
## F-statistic: 385.1 on 5 and 29994 DF,  p-value: < 0.00000000000000022
## 
## [1] "SHR"  "besu"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1103800  -760369  -681157   833417 10130159 
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)   4126062.09   25916.16 159.208 <0.0000000000000002 ***
## op_count        30249.45    1338.30  22.603 <0.0000000000000002 ***
## arg0             -542.70    1133.03  -0.479               0.632    
## arg1             1120.39    1113.21   1.006               0.314    
## op_count:arg0    -525.46      58.51  -8.981 <0.0000000000000002 ***
## op_count:arg1      42.21      57.49   0.734               0.463    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1131000 on 29994 degrees of freedom
## Multiple R-squared:  0.06643,    Adjusted R-squared:  0.06627 
## F-statistic: 426.9 on 5 and 29994 DF,  p-value: < 0.00000000000000022
## 
## [1] "SAR"  "besu"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -937537 -752382 -691909  851007 7366924 
## 
## Coefficients:
##                 Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)   4143583.37   29897.95 138.591 < 0.0000000000000002 ***
## op_count        25204.25    1543.92  16.325 < 0.0000000000000002 ***
## arg0              -79.67    1141.96  -0.070                0.944    
## arg1             -311.85    1127.83  -0.277                0.782    
## op_count:arg0    -245.13      58.97  -4.157            0.0000324 ***
## op_count:arg1      38.04      58.24   0.653                0.514    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1109000 on 29994 degrees of freedom
## Multiple R-squared:  0.05733,    Adjusted R-squared:  0.05718 
## F-statistic: 364.9 on 5 and 29994 DF,  p-value: < 0.00000000000000022
## 
## [1] "MSTORE" "besu"  
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1621512 -1306367 -1121743  -919516 15268672 
## 
## Coefficients:
##                      Estimate      Std. Error t value            Pr(>|t|)    
## (Intercept)   5017448.0931412   68426.8094813  73.326 <0.0000000000000002 ***
## op_count        33656.2601600    3533.5452474   9.525 <0.0000000000000002 ***
## arg0                3.0170568       5.3037275   0.569               0.569    
## arg1                0.5141174       5.1909987   0.099               0.921    
## op_count:arg0       0.0131320       0.2738833   0.048               0.962    
## op_count:arg1       0.0003833       0.2680620   0.001               0.999    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2743000 on 29994 degrees of freedom
## Multiple R-squared:  0.02226,    Adjusted R-squared:  0.0221 
## F-statistic: 136.6 on 5 and 29994 DF,  p-value: < 0.00000000000000022
## 
## [1] "MSTORE8" "besu"   
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1633584 -1306479 -1219350  -884711 18758774 
## 
## Coefficients:
##                    Estimate    Std. Error t value             Pr(>|t|)    
## (Intercept)   4983659.55298   63581.77601  78.382 < 0.0000000000000002 ***
## op_count        23946.91848    3283.34879   7.293     0.00000000000031 ***
## arg0                3.81809       5.33410   0.716                0.474    
## arg1               -3.57017       5.35970  -0.666                0.505    
## op_count:arg0      -0.05608       0.27545  -0.204                0.839    
## op_count:arg1       0.18133       0.27677   0.655                0.512    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2687000 on 29994 degrees of freedom
## Multiple R-squared:  0.01282,    Adjusted R-squared:  0.01265 
## F-statistic: 77.88 on 5 and 29994 DF,  p-value: < 0.00000000000000022
## 
## [1] "SWAP1" "besu" 
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1009182  -562743  -467910   616471  9783102 
## 
## Coefficients:
##                   Estimate   Std. Error t value             Pr(>|t|)    
## (Intercept)   2293012.2426   20885.3727 109.790 < 0.0000000000000002 ***
## op_count        28328.3995    1078.5160  26.266 < 0.0000000000000002 ***
## arg0             5140.3895     835.7878   6.150       0.000000000783 ***
## arg1              831.2802     813.7339   1.022                0.307    
## op_count:arg0    -170.5966      43.1599  -3.953       0.000077463012 ***
## op_count:arg1       0.9837      42.0210   0.023                0.981    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 837100 on 29994 degrees of freedom
## Multiple R-squared:  0.125,  Adjusted R-squared:  0.1248 
## F-statistic: 856.6 on 5 and 29994 DF,  p-value: < 0.00000000000000022
## 
## [1] "SWAP2" "besu" 
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1019310  -574359  -477209   608586  9138663 
## 
## Coefficients:
##                 Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)   2304964.80   21886.83 105.313 < 0.0000000000000002 ***
## op_count        28314.66    1130.23  25.052 < 0.0000000000000002 ***
## arg0             1174.37     891.81   1.317              0.18790    
## arg1             4464.84     843.84   5.291          0.000000122 ***
## op_count:arg0     -38.59      46.05  -0.838              0.40205    
## op_count:arg1    -123.54      43.58  -2.835              0.00459 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 860600 on 29994 degrees of freedom
## Multiple R-squared:  0.118,  Adjusted R-squared:  0.1179 
## F-statistic: 802.7 on 5 and 29994 DF,  p-value: < 0.00000000000000022
## 
## [1] "SWAP3" "besu" 
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -974557 -570412 -476034  606228 7326450 
## 
## Coefficients:
##                  Estimate  Std. Error t value             Pr(>|t|)    
## (Intercept)   2310447.933   20650.674 111.882 < 0.0000000000000002 ***
## op_count        28164.916    1066.396  26.411 < 0.0000000000000002 ***
## arg0             3428.964     829.305   4.135            0.0000356 ***
## arg1              580.094     808.802   0.717              0.47324    
## op_count:arg0    -118.101      42.825  -2.758              0.00582 ** 
## op_count:arg1       5.876      41.766   0.141              0.88813    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 843600 on 29994 degrees of freedom
## Multiple R-squared:  0.1283, Adjusted R-squared:  0.1282 
## F-statistic: 883.3 on 5 and 29994 DF,  p-value: < 0.00000000000000022
## 
## [1] "SWAP4" "besu" 
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1001165  -579535  -482270   598954  6618157 
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)   2435999.88   21684.62 112.338 <0.0000000000000002 ***
## op_count        24767.03    1119.79  22.118 <0.0000000000000002 ***
## arg0             -512.89     886.37  -0.579              0.5628    
## arg1            -1875.12     846.03  -2.216              0.0267 *  
## op_count:arg0      28.98      45.77   0.633              0.5266    
## op_count:arg1      42.01      43.69   0.962              0.3363    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 859500 on 29994 degrees of freedom
## Multiple R-squared:  0.1204, Adjusted R-squared:  0.1202 
## F-statistic: 820.9 on 5 and 29994 DF,  p-value: < 0.00000000000000022
## 
## [1] "SWAP5" "besu" 
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
##  -997432  -573380  -481983   608323 13518922 
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)   2380278.43   22703.00 104.844 <0.0000000000000002 ***
## op_count        27311.44    1172.38  23.296 <0.0000000000000002 ***
## arg0              883.98     844.42   1.047               0.295    
## arg1               10.26     908.43   0.011               0.991    
## op_count:arg0     -57.02      43.61  -1.308               0.191    
## op_count:arg1     -29.96      46.91  -0.639               0.523    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 854800 on 29994 degrees of freedom
## Multiple R-squared:  0.121,  Adjusted R-squared:  0.1208 
## F-statistic: 825.7 on 5 and 29994 DF,  p-value: < 0.00000000000000022
## 
## [1] "SWAP6" "besu" 
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1002151  -577676  -488135   617848  7806843 
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)   2426775.09   22783.93 106.513 <0.0000000000000002 ***
## op_count        26300.78    1176.56  22.354 <0.0000000000000002 ***
## arg0            -1438.60     866.37  -1.660              0.0968 .  
## arg1              758.09     855.85   0.886              0.3757    
## op_count:arg0      22.93      44.74   0.512              0.6083    
## op_count:arg1     -47.78      44.20  -1.081              0.2797    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 864200 on 29994 degrees of freedom
## Multiple R-squared:  0.1187, Adjusted R-squared:  0.1186 
## F-statistic: 808.3 on 5 and 29994 DF,  p-value: < 0.00000000000000022
## 
## [1] "SWAP7" "besu" 
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1011746  -585850  -491707   632744  7016892 
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)   2451892.17   22076.87 111.062 <0.0000000000000002 ***
## op_count        25855.34    1140.04  22.679 <0.0000000000000002 ***
## arg0              645.64     892.60   0.723               0.469    
## arg1             -643.95     917.91  -0.702               0.483    
## op_count:arg0     -60.26      46.09  -1.307               0.191    
## op_count:arg1      40.34      47.40   0.851               0.395    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 863700 on 29994 degrees of freedom
## Multiple R-squared:  0.1162, Adjusted R-squared:  0.116 
## F-statistic: 788.4 on 5 and 29994 DF,  p-value: < 0.00000000000000022
## 
## [1] "SWAP8" "besu" 
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -986413 -567592 -474544  608266 5449407 
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)   2387520.53   20350.66 117.319 <0.0000000000000002 ***
## op_count        25837.19    1050.90  24.586 <0.0000000000000002 ***
## arg0             -749.87     839.38  -0.893               0.372    
## arg1              400.47     857.20   0.467               0.640    
## op_count:arg0      47.68      43.35   1.100               0.271    
## op_count:arg1     -30.00      44.27  -0.678               0.498    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 848500 on 29994 degrees of freedom
## Multiple R-squared:  0.1246, Adjusted R-squared:  0.1244 
## F-statistic: 853.5 on 5 and 29994 DF,  p-value: < 0.00000000000000022
## 
## [1] "SWAP9" "besu" 
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1003402  -579136  -487734   595872 15161056 
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)   2432922.00   23609.97 103.046 <0.0000000000000002 ***
## op_count        25717.12    1219.21  21.093 <0.0000000000000002 ***
## arg0             -605.54     917.43  -0.660               0.509    
## arg1            -1053.24     872.02  -1.208               0.227    
## op_count:arg0      26.29      47.38   0.555               0.579    
## op_count:arg1     -11.73      45.03  -0.260               0.795    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 872200 on 29994 degrees of freedom
## Multiple R-squared:  0.1174, Adjusted R-squared:  0.1173 
## F-statistic: 798.1 on 5 and 29994 DF,  p-value: < 0.00000000000000022
## 
## [1] "SWAP10" "besu"  
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1027478  -592864  -505214   638552  5225823 
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)   2473927.63   23204.93 106.612 <0.0000000000000002 ***
## op_count        27072.03    1198.30  22.592 <0.0000000000000002 ***
## arg0              578.15     846.39   0.683               0.495    
## arg1            -1117.14     926.00  -1.206               0.228    
## op_count:arg0     -11.41      43.71  -0.261               0.794    
## op_count:arg1       2.97      47.82   0.062               0.950    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 877100 on 29994 degrees of freedom
## Multiple R-squared:  0.124,  Adjusted R-squared:  0.1239 
## F-statistic: 849.3 on 5 and 29994 DF,  p-value: < 0.00000000000000022
## 
## [1] "SWAP11" "besu"  
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1026664  -573809  -471586   603506  5348366 
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)   2378012.84   20658.81 115.109 <0.0000000000000002 ***
## op_count        26497.64    1066.82  24.838 <0.0000000000000002 ***
## arg0             1838.94     864.71   2.127              0.0335 *  
## arg1              -71.93     857.51  -0.084              0.9332    
## op_count:arg0     -62.89      44.65  -1.408              0.1590    
## op_count:arg1     -15.81      44.28  -0.357              0.7210    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 846900 on 29994 degrees of freedom
## Multiple R-squared:  0.1181, Adjusted R-squared:  0.1179 
## F-statistic: 803.1 on 5 and 29994 DF,  p-value: < 0.00000000000000022
## 
## [1] "SWAP12" "besu"  
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
##  -996408  -581623  -496641   613082 11071100 
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)   2387873.35   20420.47 116.935 <0.0000000000000002 ***
## op_count        27257.89    1054.51  25.849 <0.0000000000000002 ***
## arg0              322.22     840.38   0.383               0.701    
## arg1              773.92     859.14   0.901               0.368    
## op_count:arg0      -6.22      43.40  -0.143               0.886    
## op_count:arg1     -24.31      44.37  -0.548               0.584    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 871800 on 29994 degrees of freedom
## Multiple R-squared:  0.1238, Adjusted R-squared:  0.1237 
## F-statistic: 847.8 on 5 and 29994 DF,  p-value: < 0.00000000000000022
## 
## [1] "SWAP13" "besu"  
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -991913 -576805 -491979  614450 5057885 
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)   2396110.84   20351.89 117.734 <0.0000000000000002 ***
## op_count        26260.45    1050.97  24.987 <0.0000000000000002 ***
## arg0             -632.08     854.45  -0.740               0.459    
## arg1              999.80     842.78   1.186               0.236    
## op_count:arg0      45.55      44.12   1.032               0.302    
## op_count:arg1     -21.65      43.52  -0.497               0.619    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 860600 on 29994 degrees of freedom
## Multiple R-squared:  0.1262, Adjusted R-squared:  0.126 
## F-statistic:   866 on 5 and 29994 DF,  p-value: < 0.00000000000000022
## 
## [1] "SWAP14" "besu"  
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -993584 -582643 -481014  596447 5452934 
## 
## Coefficients:
##                  Estimate  Std. Error t value            Pr(>|t|)    
## (Intercept)   2388902.362   20951.690 114.020 <0.0000000000000002 ***
## op_count        25239.605    1081.941  23.328 <0.0000000000000002 ***
## arg0              938.878     901.890   1.041               0.298    
## arg1              259.662     858.265   0.303               0.762    
## op_count:arg0       6.658      46.573   0.143               0.886    
## op_count:arg1      13.475      44.321   0.304               0.761    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 864400 on 29994 degrees of freedom
## Multiple R-squared:  0.1161, Adjusted R-squared:  0.116 
## F-statistic:   788 on 5 and 29994 DF,  p-value: < 0.00000000000000022
## 
## [1] "SWAP15" "besu"  
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1047306  -601637  -499835   662841  7386117 
## 
## Coefficients:
##                  Estimate  Std. Error t value            Pr(>|t|)    
## (Intercept)   2560150.305   23055.330 111.044 <0.0000000000000002 ***
## op_count        25974.087    1190.572  21.816 <0.0000000000000002 ***
## arg0              412.282     889.838   0.463              0.6431    
## arg1            -2192.402     889.683  -2.464              0.0137 *  
## op_count:arg0       4.627      45.951   0.101              0.9198    
## op_count:arg1      59.508      45.943   1.295              0.1952    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 896000 on 29994 degrees of freedom
## Multiple R-squared:  0.1207, Adjusted R-squared:  0.1206 
## F-statistic: 823.7 on 5 and 29994 DF,  p-value: < 0.00000000000000022
## 
## [1] "SWAP16" "besu"  
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1040086  -607321  -499185   640451 12333241 
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)   2506500.41   22803.87 109.916 <0.0000000000000002 ***
## op_count        25780.37    1177.59  21.893 <0.0000000000000002 ***
## arg0             1057.30     865.91   1.221               0.222    
## arg1             -500.21     934.05  -0.536               0.592    
## op_count:arg0     -67.39      44.72  -1.507               0.132    
## op_count:arg1      20.55      48.23   0.426               0.670    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 908500 on 29994 degrees of freedom
## Multiple R-squared:  0.1024, Adjusted R-squared:  0.1022 
## F-statistic:   684 on 5 and 29994 DF,  p-value: < 0.00000000000000022
## 
## [1] "ADDMOD" "besu"  
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -5216916  -956083  -752761   517683 19398124 
## 
## Coefficients:
##                Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)   4352518.5    76969.9  56.548 < 0.0000000000000002 ***
## op_count       135025.4     3974.7  33.971 < 0.0000000000000002 ***
## arg0             -434.6     2306.2  -0.188                0.851    
## arg1             -863.2     2402.7  -0.359                0.719    
## arg2            -1269.2     2535.6  -0.501                0.617    
## op_count:arg0    3301.9      119.1  27.726 < 0.0000000000000002 ***
## op_count:arg1    3145.3      124.1  25.351 < 0.0000000000000002 ***
## op_count:arg2    -563.9      130.9  -4.307            0.0000166 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2417000 on 29992 degrees of freedom
## Multiple R-squared:  0.6139, Adjusted R-squared:  0.6138 
## F-statistic:  6813 on 7 and 29992 DF,  p-value: < 0.00000000000000022
## 
## [1] "MULMOD" "besu"  
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -5781141  -936334  -660709   462728 22991750 
## 
## Coefficients:
##                Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)   4406796.3    71368.7  61.747 < 0.0000000000000002 ***
## op_count        85015.9     3685.5  23.068 < 0.0000000000000002 ***
## arg0            -2641.3     2344.2  -1.127                0.260    
## arg1            -2797.8     2426.9  -1.153                0.249    
## arg2            -1596.6     2393.8  -0.667                0.505    
## op_count:arg0    5862.7      121.1  48.431 < 0.0000000000000002 ***
## op_count:arg1    5127.6      125.3  40.914 < 0.0000000000000002 ***
## op_count:arg2     910.8      123.6   7.368    0.000000000000177 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2475000 on 29992 degrees of freedom
## Multiple R-squared:  0.689,  Adjusted R-squared:  0.6889 
## F-statistic:  9492 on 7 and 29992 DF,  p-value: < 0.00000000000000022
## 
## [1] "CALLDATACOPY" "besu"        
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -15107114  -1934970  -1442277   -660844  49861206 
## 
## Coefficients:
##                   Estimate   Std. Error t value             Pr(>|t|)    
## (Intercept)   4974254.8889  158220.6228  31.439 < 0.0000000000000002 ***
## op_count        -5591.1860    8170.4778  -0.684              0.49378    
## arg0               -0.3802      11.0433  -0.034              0.97254    
## arg1               -0.2241      11.4327  -0.020              0.98436    
## arg2               29.5696      10.4767   2.822              0.00477 ** 
## op_count:arg0      -0.2283       0.5703  -0.400              0.68887    
## op_count:arg1       0.4976       0.5904   0.843              0.39930    
## op_count:arg2     138.1495       0.5410 255.353 < 0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5647000 on 29992 degrees of freedom
## Multiple R-squared:  0.9195, Adjusted R-squared:  0.9194 
## F-statistic: 4.891e+04 on 7 and 29992 DF,  p-value: < 0.00000000000000022
## 
## [1] "CODECOPY" "besu"    
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -8496477 -2074176 -1466956  -732798 58298568 
## 
## Coefficients:
##                   Estimate   Std. Error t value            Pr(>|t|)    
## (Intercept)   4984579.2788  176653.4442  28.217 <0.0000000000000002 ***
## op_count       -16522.0586    9122.3446  -1.811              0.0701 .  
## arg0               -6.1938      11.4130  -0.543              0.5873    
## arg1               -7.4745      11.2843  -0.662              0.5077    
## arg2               32.6095      11.5618   2.820              0.0048 ** 
## op_count:arg0       0.4906       0.5894   0.832              0.4052    
## op_count:arg1       1.2453       0.5827   2.137              0.0326 *  
## op_count:arg2     138.1938       0.5970 231.462 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5818000 on 29992 degrees of freedom
## Multiple R-squared:  0.9105, Adjusted R-squared:  0.9105 
## F-statistic: 4.359e+04 on 7 and 29992 DF,  p-value: < 0.00000000000000022
## 
## [1] "RETURNDATACOPY" "besu"          
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -5050481 -2247727 -1799763  -773493 55662894 
## 
## Coefficients:
##                    Estimate    Std. Error t value            Pr(>|t|)    
## (Intercept)   10563669.5102   167049.2299  63.237 <0.0000000000000002 ***
## op_count          8698.3266     8626.3851   1.008               0.313    
## arg0                -2.3435       13.1361  -0.178               0.858    
## arg1                 5.0963       12.0631   0.422               0.673    
## arg2                16.8539       11.8162   1.426               0.154    
## op_count:arg0        0.6102        0.6783   0.900               0.368    
## op_count:arg1       -0.2363        0.6229  -0.379               0.704    
## op_count:arg2      137.0547        0.6102 224.612 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6253000 on 29992 degrees of freedom
## Multiple R-squared:  0.8997, Adjusted R-squared:  0.8997 
## F-statistic: 3.844e+04 on 7 and 29992 DF,  p-value: < 0.00000000000000022
## 
## [1] "DIV"  "besu"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2667988 -1028701  -731367   899824 15547075 
## 
## Coefficients:
##                        Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)             3604593      28511 126.430 < 0.0000000000000002 ***
## op_count                  77385       1004  77.061 < 0.0000000000000002 ***
## arg0                      24134       1208  19.981 < 0.0000000000000002 ***
## arg1                       9438       1166   8.095 0.000000000000000595 ***
## op_count:expensiveTRUE    53490       1277  41.896 < 0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1655000 on 29995 degrees of freedom
## Multiple R-squared:  0.423,  Adjusted R-squared:  0.4229 
## F-statistic:  5497 on 4 and 29995 DF,  p-value: < 0.00000000000000022
## 
## [1] "MOD"  "besu"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -3495886 -1267206  -736693   349221 22298811 
## 
## Coefficients:
##                        Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)             3408927      38376   88.83 <0.0000000000000002 ***
## op_count                 130951       1385   94.53 <0.0000000000000002 ***
## arg0                      29930       1598   18.73 <0.0000000000000002 ***
## arg1                      21192       1601   13.24 <0.0000000000000002 ***
## op_count:expensiveTRUE    68191       1714   39.78 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2203000 on 29995 degrees of freedom
## Multiple R-squared:  0.4977, Adjusted R-squared:  0.4976 
## F-statistic:  7430 on 4 and 29995 DF,  p-value: < 0.00000000000000022
## 
## [1] "SDIV" "besu"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -3226217 -1252731  -771650   364716 19174704 
## 
## Coefficients:
##                        Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)             3583515      37402  95.811 <0.0000000000000002 ***
## op_count                 133663       1361  98.188 <0.0000000000000002 ***
## arg0                      26566       1634  16.255 <0.0000000000000002 ***
## arg1                      13995       1604   8.726 <0.0000000000000002 ***
## op_count:expensiveTRUE    61719       1716  35.961 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2212000 on 29995 degrees of freedom
## Multiple R-squared:  0.4847, Adjusted R-squared:  0.4846 
## F-statistic:  7053 on 4 and 29995 DF,  p-value: < 0.00000000000000022
## 
## [1] "SMOD" "besu"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -3445493 -1272284  -759793   402957 21640365 
## 
## Coefficients:
##                        Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)             3470760      38159   90.95 <0.0000000000000002 ***
## op_count                 132827       1296  102.46 <0.0000000000000002 ***
## arg0                      30356       1570   19.34 <0.0000000000000002 ***
## arg1                      19181       1640   11.70 <0.0000000000000002 ***
## op_count:expensiveTRUE    66527       1696   39.22 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2196000 on 29995 degrees of freedom
## Multiple R-squared:  0.4891, Adjusted R-squared:  0.489 
## F-statistic:  7179 on 4 and 29995 DF,  p-value: < 0.00000000000000022
## 
## [1] "ADDMOD" "besu"  
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -4795315 -1257097  -689450   158932 20519733 
## 
## Coefficients:
##                        Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)             3047744      50669   60.15 <0.0000000000000002 ***
## op_count                 160454       1827   87.84 <0.0000000000000002 ***
## arg0                      24500       1497   16.37 <0.0000000000000002 ***
## arg1                      20558       1560   13.18 <0.0000000000000002 ***
## arg2                      28360       1718   16.50 <0.0000000000000002 ***
## op_count:expensiveTRUE   105343       1943   54.22 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2364000 on 29994 degrees of freedom
## Multiple R-squared:  0.6308, Adjusted R-squared:  0.6307 
## F-statistic: 1.025e+04 on 5 and 29994 DF,  p-value: < 0.00000000000000022
## 
## [1] "MULMOD" "besu"  
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -4563522 -1345778  -580327   406749 24792300 
## 
## Coefficients:
##                        Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)             1869277      49527   37.74 <0.0000000000000002 ***
## op_count                 174246       2232   78.07 <0.0000000000000002 ***
## arg0                      54933       1601   34.30 <0.0000000000000002 ***
## arg1                      47919       1628   29.44 <0.0000000000000002 ***
## arg2                      47425       1664   28.50 <0.0000000000000002 ***
## op_count:expensiveTRUE   127019       2308   55.03 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2509000 on 29994 degrees of freedom
## Multiple R-squared:  0.6802, Adjusted R-squared:  0.6802 
## F-statistic: 1.276e+04 on 5 and 29994 DF,  p-value: < 0.00000000000000022
proceed_with_opcodes = unique(first_pass[which(first_pass$has_impacting == 'TRUE'), 'opcode'])

models_with_args_automatic = first_pass[which(first_pass$has_impacting == 'TRUE'), c('opcode', 'env')]
models_with_expensive_automatic = first_pass[which(!is.na(first_pass$expensive_ns)), c('opcode', 'env')]

first_pass[which(first_pass$has_impacting == 'TRUE'), ]
##            opcode  env has_significant has_impacting estimate_marginal_ns
## 23            ADD besu            TRUE          TRUE     59139.7381507484
## 26            DIV besu            TRUE          TRUE     64440.8136568123
## 27           SDIV besu            TRUE          TRUE     117636.561326761
## 28            MOD besu            TRUE          TRUE     106716.358973534
## 29           SMOD besu            TRUE          TRUE     107143.643084154
## 30            EXP besu            TRUE          TRUE    -65006.0134807013
## 62         ADDMOD besu            TRUE          TRUE     135025.401335389
## 63         MULMOD besu            TRUE          TRUE     85015.9150731655
## 64   CALLDATACOPY besu            TRUE          TRUE    -5591.18599445232
## 65       CODECOPY besu            TRUE          TRUE    -16522.0586345979
## 66 RETURNDATACOPY besu            TRUE          TRUE      8698.3265504224
##             arg0_ns          arg1_ns          arg2_ns     expensive_ns
## 23 995.346364457664 1029.11794431736             <NA>             <NA>
## 26 3280.95484173404             <NA>             <NA> 53490.4167445755
## 27  3813.4619404136             <NA>             <NA> 61718.8876260387
## 28 4043.64698117967             <NA>             <NA> 68190.6096058811
## 29 4144.17600834556             <NA>             <NA> 66527.0478967747
## 30             <NA> 161222.132965171             <NA>             <NA>
## 62 3301.93326258818 3145.32858503558             <NA> 105343.306489307
## 63 5862.73515573292 5127.56253678244             <NA> 127018.823017713
## 64             <NA>             <NA> 138.149495987096             <NA>
## 65             <NA>             <NA> 138.193841682033             <NA>
## 66             <NA>             <NA> 137.054701218205             <NA>
##           arg0_ns_raw        arg1_ns_raw       arg2_ns_raw expensive_ns_raw
## 23   995.346364457664   1029.11794431736              <NA>             <NA>
## 26   3280.95484173404  -886.715655686377              <NA> 53490.4167445755
## 27    3813.4619404136  -1002.85974373072              <NA> 61718.8876260387
## 28   4043.64698117967  -612.852985723579              <NA> 68190.6096058811
## 29   4144.17600834556  -622.302702197993              <NA> 66527.0478967747
## 30   27401.8013551266   161222.132965171              <NA>             <NA>
## 62   3301.93326258818   3145.32858503558 -563.939160832652 105343.306489307
## 63   5862.73515573292   5127.56253678244  910.816302457666 127018.823017713
## 64 -0.228337140849684  0.497624449467041  138.149495987096             <NA>
## 65   0.49056884283877   1.24526323664146  138.193841682033             <NA>
## 66  0.610182649790944 -0.236270137314065  137.054701218205             <NA>
##                                             arg0_ns_p
## 23 0.000000000000000000000000000000000917354071418478
## 26                              3.28435473981944e-317
## 27                              2.06403979558713e-245
## 28                              1.02320297825666e-276
## 29                              1.01085126358467e-306
## 30                0.000000000000000000415945203533126
## 62                              4.39209729822195e-167
## 63                                                  0
## 64                                  0.688865395380998
## 65                                  0.405207676519483
## 66                                  0.368385662701539
##                                                arg1_ns_p
## 23 0.000000000000000000000000000000000000144651867702733
## 26            0.0000000000000000000000000180727443383065
## 27                    0.00000000000000000104671059335752
## 28                              0.0000000682395797100088
## 29                               0.000000182090452412657
## 30                                                     0
## 62                                 2.65377869638366e-140
## 63                                                     0
## 64                                     0.399299123027796
## 65                                    0.0326064384982231
## 66                                     0.704479981635845
##                       arg2_ns_p        expensive_ns_p
## 23                         <NA>                  <NA>
## 26                         <NA>                     0
## 27                         <NA> 2.64958013758168e-277
## 28                         <NA>                     0
## 29                         <NA>                     0
## 30                         <NA>                  <NA>
## 62        0.0000166066591397799                     0
## 63 0.00000000000017740396346513                     0
## 64                            0                  <NA>
## 65                            0                  <NA>
## 66                            0                  <NA>

We inspect the automatic choice of models, but then coerce the choice to a fixed list. We drop the division OPCODEs (DIV etc.), because their arguments only seem to have an indirect impact via the fact that x / y is trivial if x < y. This makes the DIV(x, y) appear costlier for large x and cheaper for large y.

models_with_args = data.frame(opcode="EXP", env=env, arg=1)
first_pass$arg1_ns[is.na(first_pass$arg1_ns) & first_pass$opcode=="EXP" & first_pass$env==env] <- first_pass$arg1_ns_raw[is.na(first_pass$arg1_ns) & first_pass$opcode=="EXP" & first_pass$env==env]
models_with_args = rbind(models_with_args, data.frame(opcode="CALLDATACOPY", env=env, arg=2))
first_pass$arg2_ns[is.na(first_pass$arg2_ns) & first_pass$opcode=="CALLDATACOPY" & first_pass$env==env] <- first_pass$arg2_ns_raw[is.na(first_pass$arg2_ns) & first_pass$opcode=="CALLDATACOPY" & first_pass$env==env]
models_with_args = rbind(models_with_args, data.frame(opcode="CODECOPY", env=env, arg=2))
first_pass$arg2_ns[is.na(first_pass$arg2_ns) & first_pass$opcode=="CODECOPY" & first_pass$env==env] <- first_pass$arg2_ns_raw[is.na(first_pass$arg2_ns) & first_pass$opcode=="CODECOPY" & first_pass$env==env]
models_with_args = rbind(models_with_args, data.frame(opcode="RETURNDATACOPY", env=env, arg=2))
first_pass$arg2_ns[is.na(first_pass$arg2_ns) & first_pass$opcode=="RETURNDATACOPY" & first_pass$env==env] <- first_pass$arg2_ns_raw[is.na(first_pass$arg2_ns) & first_pass$opcode=="RETURNDATACOPY" & first_pass$env==env]

models_with_expensive = data.frame(opcode="DIV", env=env)
models_with_expensive = rbind(models_with_expensive, data.frame(opcode="SDIV", env=env))
models_with_expensive = rbind(models_with_expensive, data.frame(opcode="MOD", env=env))
models_with_expensive = rbind(models_with_expensive, data.frame(opcode="SMOD", env=env))
models_with_expensive = rbind(models_with_expensive, data.frame(opcode="ADDMOD", env=env))
models_with_expensive = rbind(models_with_expensive, data.frame(opcode="MULMOD", env=env))

Detailed analysis for selected OPCODEs

We go through all the OPCODEs which turned out to have impacting arguments in the automatic discrimination procedure, and we plot some validation plots to inspect these relationships.

# Takes the results data frame and checks which argument indices (0, 1, etc.)
# turned out to be impacting
get_impact_args_for <- function(df, opcode, env) {
  if (opcode %in% nullary_opcodes) {
    return(c())
  }
  args = c()
  for (n in 0:2) {
    argname = paste0('arg', n, '_ns')
    if (!is.na(df[which(df$opcode==opcode & df$env==env), argname])) {
      args = c(n, args)
    }
  }
  return(rev(args))
}

# same as `get_impact_args_for` but gets all the argument indices
get_args_for <- function(df, opcode, env) {
  if (opcode %in% unary_opcodes) {
    c(0)
  } else if (opcode %in% binary_opcodes) {
    c(0, 1)
  } else if (opcode %in% ternary_opcodes) {
    c(0, 1, 2)
  }
}

# Builds a final model formula to estimate, based on whether the arguments
# came out impactful from the automatic discrimination process.
get_model_formula_for <- function(df, opcode, env) {
  args = get_args_for(df, opcode, env)
  argnames = paste0('arg', args)
  args_formula = paste0(argnames, collapse=' + ')
  
  impact_args = get_impact_args_for(df, opcode, env)
  if (opcode %in% nullary_opcodes) {
    as.formula('measure_total_time_ns ~ op_count')
  } else if (is.null(impact_args)) {
    as.formula(paste0('measure_total_time_ns ~ op_count +  ', args_formula))
  } else {
  arg_op_count_names = paste0('arg', impact_args, ':op_count')
  arg_op_counts_formula = paste0(arg_op_count_names, collapse=' + ')
  as.formula(paste0('measure_total_time_ns ~ op_count +  ', args_formula, ' + ', arg_op_counts_formula))
  }
}

# Same as `get_model_formula_for` but gauged towards the division OPCODEs specifically.
get_expensive_model_formula_for <- function(df, opcode, env) {
  args = get_args_for(df, opcode, env)
  argnames = paste0('arg', args)
  args_formula = paste0(argnames, collapse=' + ')
  as.formula(paste0('measure_total_time_ns ~ op_count +  ', args_formula, ' + expensive:op_count'))
}

# Same as `get_model_formula_for` but returns the formula to provide the `aggregate` function with.
get_aggregate_formula_for <- function(df, opcode, env) {
  args = get_args_for(df, opcode, env)
  argnames = paste0('arg', args)
  args_formula = paste0(argnames, collapse=' * ')
  as.formula(paste0('measure_total_time_ns ~ op_count * env * opcode * ', args_formula))
}

# Presents the diagnostic plots for a given slice of the data
plot_model <- function(df, opcode, env, use_mean) {
  if (missing(use_mean)) {
    use_mean = FALSE
  }
  if (use_mean) {
    df = aggregate(get_aggregate_formula_for(df, opcode, env), measurements[which(df$opcode==opcode & df$env==env), ], mean, na.action=na.pass)
  }
  model = arg_lm(df, opcode, env, get_model_formula_for(first_pass, opcode, env))
  print(c(opcode, env))
  print(summary(model))
  
  par(mfrow=c(2,2))
  plot(model)
  
  plot_data = df[which(df$env == env & df$opcode == opcode & df$op_count == max(df$op_count)), ]
  if (opcode %in% binary_opcodes) {
    par(mfrow=c(1,1))
    
    decreasing_colors = heat.colors(nrow(plot_data))
    plot_data=plot_data[order(plot_data$measure_total_time_ns, decreasing=TRUE), ]
    with(plot_data, plot(arg0, arg1, col=decreasing_colors, pch=19))
  }
  title(main=paste(opcode, env))
}

Using the functions defined above, we proceed to plot the diagnostic plots of the arguments models.

for (env in all_envs) {
  for (opcode in proceed_with_opcodes) {
    plot_model(measurements, opcode, env, use_mean=TRUE)
  } 
}
## [1] "ADD"  "besu"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -311229  -97213  -13692   82015  632443 
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)   4143543.87   25228.48 164.241 <0.0000000000000002 ***
## op_count        58952.29    1302.79  45.251 <0.0000000000000002 ***
## arg0             1239.62     996.15   1.244               0.214    
## arg1             -293.04     980.70  -0.299               0.765    
## op_count:arg0    1004.33      51.44  19.524 <0.0000000000000002 ***
## op_count:arg1    1042.80      50.64  20.591 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 133800 on 528 degrees of freedom
## Multiple R-squared:  0.9873, Adjusted R-squared:  0.9872 
## F-statistic:  8217 on 5 and 528 DF,  p-value: < 0.00000000000000022

## [1] "DIV"  "besu"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1405120  -214343    -3010   248981  1157267 
## 
## Coefficients:
##                Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)   4408356.7    61906.5  71.210 <0.0000000000000002 ***
## op_count        50088.0     2798.7  17.897 <0.0000000000000002 ***
## arg0              647.9     2895.3   0.224               0.823    
## arg1           -15554.4     1773.6  -8.770 <0.0000000000000002 ***
## op_count:arg0    3250.4      149.5  21.740 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 390600 on 547 degrees of freedom
## Multiple R-squared:  0.9282, Adjusted R-squared:  0.9276 
## F-statistic:  1767 on 4 and 547 DF,  p-value: < 0.00000000000000022

## [1] "SDIV" "besu"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1957906  -282707   -41229   338623  2069148 
## 
## Coefficients:
##               Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)    4480895      84495  53.031 < 0.0000000000000002 ***
## op_count        102730       3843  26.729 < 0.0000000000000002 ***
## arg0              1457       3952   0.369                0.713    
## arg1            -14986       2538  -5.905        0.00000000632 ***
## op_count:arg0     3717        204  18.220 < 0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 537200 on 529 degrees of freedom
## Multiple R-squared:  0.9401, Adjusted R-squared:  0.9397 
## F-statistic:  2077 on 4 and 529 DF,  p-value: < 0.00000000000000022

## [1] "MOD"  "besu"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2103617  -261751   -77173   432410  1353184 
## 
## Coefficients:
##                Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)   4385561.3    95693.3  45.829 < 0.0000000000000002 ***
## op_count        96205.3     4380.2  21.963 < 0.0000000000000002 ***
## arg0             1595.5     4346.3   0.367             0.713698    
## arg1            -9314.5     2774.9  -3.357             0.000846 ***
## op_count:arg0    4153.6      224.4  18.508 < 0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 588100 on 523 degrees of freedom
## Multiple R-squared:  0.9333, Adjusted R-squared:  0.9328 
## F-statistic:  1830 on 4 and 523 DF,  p-value: < 0.00000000000000022

## [1] "SMOD" "besu"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2064263  -229388   -56209   374149  1410952 
## 
## Coefficients:
##                Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)   4398303.2    87892.7  50.042 < 0.0000000000000002 ***
## op_count        96889.7     3841.3  25.223 < 0.0000000000000002 ***
## arg0             -211.8     4005.0  -0.053              0.95785    
## arg1            -8070.1     2745.6  -2.939              0.00343 ** 
## op_count:arg0    4192.3      206.6  20.293 < 0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 561300 on 550 degrees of freedom
## Multiple R-squared:  0.9369, Adjusted R-squared:  0.9364 
## F-statistic:  2040 on 4 and 550 DF,  p-value: < 0.00000000000000022

## [1] "EXP"  "besu"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##        Min         1Q     Median         3Q        Max 
## -141749734  -26729920    -821390    8086960  202423443 
## 
## Coefficients:
##               Estimate Std. Error t value          Pr(>|t|)    
## (Intercept)    1092082    8966723   0.122             0.903    
## op_count        373932     410840   0.910             0.363    
## arg0            213388     278090   0.767             0.443    
## arg1            -21607     422329  -0.051             0.959    
## op_count:arg1   164786      21768   7.570 0.000000000000168 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 59920000 on 526 degrees of freedom
## Multiple R-squared:  0.3984, Adjusted R-squared:  0.3938 
## F-statistic: 87.07 on 4 and 526 DF,  p-value: < 0.00000000000000022

## [1] "ADDMOD" "besu"  
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -3715155  -316482   -73133   586645  1760055 
## 
## Coefficients:
##                Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)   4506828.0   169745.7  26.550 <0.0000000000000002 ***
## op_count       124738.1     7916.3  15.757 <0.0000000000000002 ***
## arg0             -596.9     5745.8  -0.104              0.9173    
## arg1            -1550.5     5973.6  -0.260              0.7953    
## arg2            -9728.3     3995.9  -2.435              0.0152 *  
## op_count:arg0    3312.8      296.7  11.166 <0.0000000000000002 ***
## op_count:arg1    3191.1      308.0  10.360 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 851700 on 593 degrees of freedom
## Multiple R-squared:  0.9283, Adjusted R-squared:  0.9276 
## F-statistic:  1280 on 6 and 593 DF,  p-value: < 0.00000000000000022

## [1] "MULMOD" "besu"  
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -4525175  -286269   -25308   685056  1613798 
## 
## Coefficients:
##                Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)   4185313.5   171333.9  24.428 < 0.0000000000000002 ***
## op_count       100166.0     8223.1  12.181 < 0.0000000000000002 ***
## arg0            -2151.2     6203.0  -0.347              0.72887    
## arg1            -3191.0     6386.2  -0.500              0.61749    
## arg2            12396.8     3997.0   3.102              0.00202 ** 
## op_count:arg0    5800.9      320.2  18.114 < 0.0000000000000002 ***
## op_count:arg1    5149.1      329.7  15.616 < 0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 921100 on 590 degrees of freedom
## Multiple R-squared:  0.9419, Adjusted R-squared:  0.9413 
## F-statistic:  1594 on 6 and 590 DF,  p-value: < 0.00000000000000022

## [1] "CALLDATACOPY" "besu"        
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2504934  -342990    -7740   383485  2042814 
## 
## Coefficients:
##                   Estimate   Std. Error t value             Pr(>|t|)    
## (Intercept)   4935826.9573  104877.8221  47.063 < 0.0000000000000002 ***
## op_count        -3029.3239    4250.2438  -0.713             0.476286    
## arg0               -3.8052       5.8124  -0.655             0.512928    
## arg1                7.2403       6.0173   1.203             0.229362    
## arg2               30.2297       8.6966   3.476             0.000546 ***
## op_count:arg2     138.1055       0.4483 308.042 < 0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 664600 on 594 degrees of freedom
## Multiple R-squared:  0.9988, Adjusted R-squared:  0.9988 
## F-statistic: 9.887e+04 on 5 and 594 DF,  p-value: < 0.00000000000000022

## [1] "CODECOPY" "besu"    
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2491115  -335076     9448   388919  1973635 
## 
## Coefficients:
##                  Estimate  Std. Error t value             Pr(>|t|)    
## (Intercept)   4762131.797  110749.848  42.999 < 0.0000000000000002 ***
## op_count        -1692.226    4403.488  -0.384             0.700899    
## arg0                1.165       5.743   0.203             0.839358    
## arg1               11.204       5.678   1.973             0.048945 *  
## arg2               35.165       9.152   3.842             0.000135 ***
## op_count:arg2     138.024       0.471 293.038 < 0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 654600 on 594 degrees of freedom
## Multiple R-squared:  0.9988, Adjusted R-squared:  0.9988 
## F-statistic: 9.639e+04 on 5 and 594 DF,  p-value: < 0.00000000000000022

## [1] "RETURNDATACOPY" "besu"          
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2754809  -400024    -3629   492052  1931178 
## 
## Coefficients:
##                    Estimate    Std. Error t value            Pr(>|t|)    
## (Intercept)   10530020.3647   110994.8958  94.869 <0.0000000000000002 ***
## op_count         10941.6029     4652.3297   2.352              0.0190 *  
## arg0                 6.8092        6.7873   1.003              0.3162    
## arg1                 1.5522        6.2329   0.249              0.8034    
## arg2                16.4646        9.6428   1.707              0.0883 .  
## op_count:arg2      137.0807        0.4976 275.492 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 722400 on 594 degrees of freedom
## Multiple R-squared:  0.9985, Adjusted R-squared:  0.9985 
## F-statistic: 8.063e+04 on 5 and 594 DF,  p-value: < 0.00000000000000022

Producing the final estimates

We’d like to only estimate using the arg-variables in models, where this actually matters to avoid spurious impact of insignificant variables.

We’ll estimate a model with only those argument variables, where they turned out impacting. For those where no argument variable was impacting, we’ll only estimate the marginal increase (corresponding to the constant cost of an OPCODE).

# `results_df` is assumed to have the columns as the `estimates` data frame has (see below)
add_non_arg_model_estimates <- function(model, results_df, env, opcode) {
  pure_op_count_coeff = summary(model)$coefficients["op_count", 1]
  args_ns = c(NA, NA, NA)
  args_ns_stderr = c(NA, NA, NA)
  results_df[nrow(results_df) + 1, ] = c(opcode, env, FALSE, FALSE, pure_op_count_coeff, args_ns, NA, args_ns_stderr, NA)
  return(results_df)
}
add_arg_model_estimates <- function(model, opcode, env, results_df, df) {
  all_coefficients = summary(model)$coefficients
  arg_coefficients = all_coefficients[!(row.names(all_coefficients) %in% c("op_count", "(Intercept)", "arg0", "arg1", "arg2")),]
  pure_op_count_coeff = all_coefficients["op_count", 1]
  # will be filled if any is impacting
  args_ns = c(NA, NA, NA)
  args_ns_stderr = c(NA, NA, NA)
  
  impact_args = get_impact_args_for(df, opcode, env)
  arg_op_count_names = paste0('op_count:arg', impact_args)

  args_ns[impact_args + 1] = all_coefficients[arg_op_count_names, 'Estimate']
  args_ns_stderr[impact_args + 1] = all_coefficients[arg_op_count_names, 'Std. Error']
  results_df[nrow(results_df) + 1, ] = c(opcode, env, TRUE, TRUE, pure_op_count_coeff, args_ns, NA, args_ns_stderr, NA)
  return(results_df)
}
add_expensive_model_estimates <- function(model, opcode, env, results_df, df) {
  all_coefficients = summary(model)$coefficients
  pure_op_count_coeff = all_coefficients["op_count", 1]
  args_ns = c(NA, NA, NA)
  args_ns_stderr = c(NA, NA, NA)
  expensive =  all_coefficients['op_count:expensiveTRUE', 'Estimate']
  expensive_stderr = all_coefficients['op_count:expensiveTRUE', 'Std. Error']
  results_df[nrow(results_df) + 1, ] = c(opcode, env, TRUE, TRUE, pure_op_count_coeff, args_ns, expensive, args_ns_stderr, expensive_stderr)
  return(results_df)
}
estimates = data.frame(matrix(ncol = 13, nrow = 0))
colnames(estimates) <- c('opcode', 'env', 'has_significant', 'has_impacting', 'estimate_marginal_ns',
                         'arg0_ns', 'arg1_ns', 'arg2_ns', 'expensive_ns', 'arg0_ns_stderr', 'arg1_ns_stderr', 'arg2_ns_stderr', 'expensive_ns_stderr')

for (env in all_envs) {
  for (opcode in all_opcodes) {
    is_modeled_with_args = nrow(merge(data.frame(opcode=opcode, env=env), models_with_args)) > 0
    is_modeled_with_expensive = nrow(merge(data.frame(opcode=opcode, env=env), models_with_expensive)) > 0
    if (is_modeled_with_expensive) {
      model = arg_lm(measurements, opcode, env, get_expensive_model_formula_for(first_pass, opcode, env))
      estimates = add_expensive_model_estimates(model, opcode, env, estimates, first_pass)
    } else if (is_modeled_with_args) {
      model = arg_lm(measurements, opcode, env, get_model_formula_for(first_pass, opcode, env))
      estimates = add_arg_model_estimates(model, opcode, env, estimates, first_pass)
    } else {
      model = arg_lm(measurements, opcode, env, get_model_formula_for(first_pass, opcode, env))
      estimates = add_non_arg_model_estimates(model, estimates, env, opcode)
    }
    print(c(opcode, env))
    print(summary(model))
  }
}
## [1] "ADD"  "besu"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1452551  -971335  -772328   861188 17081974 
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)   4141012.16   40336.52 102.662 <0.0000000000000002 ***
## op_count        59139.74    2082.97  28.392 <0.0000000000000002 ***
## arg0             1286.96    1589.48   0.810               0.418    
## arg1             -115.45    1553.61  -0.074               0.941    
## op_count:arg0     995.35      82.08  12.126 <0.0000000000000002 ***
## op_count:arg1    1029.12      80.23  12.827 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1569000 on 29994 degrees of freedom
## Multiple R-squared:  0.3587, Adjusted R-squared:  0.3586 
## F-statistic:  3356 on 5 and 29994 DF,  p-value: < 0.00000000000000022
## 
## [1] "MUL"  "besu"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1333434  -968248  -748914   822791 18343718 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 4144193.8    28097.4 147.494 <0.0000000000000002 ***
## op_count      90509.6      745.4 121.425 <0.0000000000000002 ***
## arg0           -307.5     1019.1  -0.302               0.763    
## arg1           -142.8     1021.3  -0.140               0.889    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1581000 on 29996 degrees of freedom
## Multiple R-squared:  0.3296, Adjusted R-squared:  0.3295 
## F-statistic:  4915 on 3 and 29996 DF,  p-value: < 0.00000000000000022
## 
## [1] "SUB"  "besu"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1233256  -941461  -761567   882688  9947946 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 4154179.0    26642.2 155.925 <0.0000000000000002 ***
## op_count      57096.7      703.0  81.214 <0.0000000000000002 ***
## arg0           -329.9      965.1  -0.342               0.732    
## arg1            311.7      960.3   0.325               0.745    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1491000 on 29996 degrees of freedom
## Multiple R-squared:  0.1803, Adjusted R-squared:  0.1802 
## F-statistic:  2199 on 3 and 29996 DF,  p-value: < 0.00000000000000022
## 
## [1] "DIV"  "besu"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2667988 -1028701  -731367   899824 15547075 
## 
## Coefficients:
##                        Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)             3604593      28511 126.430 < 0.0000000000000002 ***
## op_count                  77385       1004  77.061 < 0.0000000000000002 ***
## arg0                      24134       1208  19.981 < 0.0000000000000002 ***
## arg1                       9438       1166   8.095 0.000000000000000595 ***
## op_count:expensiveTRUE    53490       1277  41.896 < 0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1655000 on 29995 degrees of freedom
## Multiple R-squared:  0.423,  Adjusted R-squared:  0.4229 
## F-statistic:  5497 on 4 and 29995 DF,  p-value: < 0.00000000000000022
## 
## [1] "SDIV" "besu"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -3226217 -1252731  -771650   364716 19174704 
## 
## Coefficients:
##                        Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)             3583515      37402  95.811 <0.0000000000000002 ***
## op_count                 133663       1361  98.188 <0.0000000000000002 ***
## arg0                      26566       1634  16.255 <0.0000000000000002 ***
## arg1                      13995       1604   8.726 <0.0000000000000002 ***
## op_count:expensiveTRUE    61719       1716  35.961 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2212000 on 29995 degrees of freedom
## Multiple R-squared:  0.4847, Adjusted R-squared:  0.4846 
## F-statistic:  7053 on 4 and 29995 DF,  p-value: < 0.00000000000000022
## 
## [1] "MOD"  "besu"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -3495886 -1267206  -736693   349221 22298811 
## 
## Coefficients:
##                        Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)             3408927      38376   88.83 <0.0000000000000002 ***
## op_count                 130951       1385   94.53 <0.0000000000000002 ***
## arg0                      29930       1598   18.73 <0.0000000000000002 ***
## arg1                      21192       1601   13.24 <0.0000000000000002 ***
## op_count:expensiveTRUE    68191       1714   39.78 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2203000 on 29995 degrees of freedom
## Multiple R-squared:  0.4977, Adjusted R-squared:  0.4976 
## F-statistic:  7430 on 4 and 29995 DF,  p-value: < 0.00000000000000022
## 
## [1] "SMOD" "besu"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -3445493 -1272284  -759793   402957 21640365 
## 
## Coefficients:
##                        Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)             3470760      38159   90.95 <0.0000000000000002 ***
## op_count                 132827       1296  102.46 <0.0000000000000002 ***
## arg0                      30356       1570   19.34 <0.0000000000000002 ***
## arg1                      19181       1640   11.70 <0.0000000000000002 ***
## op_count:expensiveTRUE    66527       1696   39.22 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2196000 on 29995 degrees of freedom
## Multiple R-squared:  0.4891, Adjusted R-squared:  0.489 
## F-statistic:  7179 on 4 and 29995 DF,  p-value: < 0.00000000000000022
## 
## [1] "ADDMOD" "besu"  
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -4795315 -1257097  -689450   158932 20519733 
## 
## Coefficients:
##                        Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)             3047744      50669   60.15 <0.0000000000000002 ***
## op_count                 160454       1827   87.84 <0.0000000000000002 ***
## arg0                      24500       1497   16.37 <0.0000000000000002 ***
## arg1                      20558       1560   13.18 <0.0000000000000002 ***
## arg2                      28360       1718   16.50 <0.0000000000000002 ***
## op_count:expensiveTRUE   105343       1943   54.22 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2364000 on 29994 degrees of freedom
## Multiple R-squared:  0.6308, Adjusted R-squared:  0.6307 
## F-statistic: 1.025e+04 on 5 and 29994 DF,  p-value: < 0.00000000000000022
## 
## [1] "MULMOD" "besu"  
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -4563522 -1345778  -580327   406749 24792300 
## 
## Coefficients:
##                        Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)             1869277      49527   37.74 <0.0000000000000002 ***
## op_count                 174246       2232   78.07 <0.0000000000000002 ***
## arg0                      54933       1601   34.30 <0.0000000000000002 ***
## arg1                      47919       1628   29.44 <0.0000000000000002 ***
## arg2                      47425       1664   28.50 <0.0000000000000002 ***
## op_count:expensiveTRUE   127019       2308   55.03 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2509000 on 29994 degrees of freedom
## Multiple R-squared:  0.6802, Adjusted R-squared:  0.6802 
## F-statistic: 1.276e+04 on 5 and 29994 DF,  p-value: < 0.00000000000000022
## 
## [1] "EXP"  "besu"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##        Min         1Q     Median         3Q        Max 
## -142766432  -29784969   -1694531    7186457  238703191 
## 
## Coefficients:
##               Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)   -1845061    1216356  -1.517                0.129    
## op_count        343496      55747   6.162       0.000000000729 ***
## arg0            409575      37594  10.895 < 0.0000000000000002 ***
## arg1            -36807      57241  -0.643                0.520    
## op_count:arg1   163575       2951  55.427 < 0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 61290000 on 29995 degrees of freedom
## Multiple R-squared:  0.3812, Adjusted R-squared:  0.3811 
## F-statistic:  4619 on 4 and 29995 DF,  p-value: < 0.00000000000000022
## 
## [1] "SIGNEXTEND" "besu"      
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1313668  -970724  -760188   822479 16467828 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 4158888.1    26394.2 157.568 <0.0000000000000002 ***
## op_count     145660.9      737.1 197.609 <0.0000000000000002 ***
## arg0           -333.8      963.9  -0.346               0.729    
## arg1           -441.8      994.2  -0.444               0.657    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1564000 on 29996 degrees of freedom
## Multiple R-squared:  0.5656, Adjusted R-squared:  0.5655 
## F-statistic: 1.302e+04 on 3 and 29996 DF,  p-value: < 0.00000000000000022
## 
## [1] "LT"   "besu"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1176309  -906886  -749931  1016181 11119971 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 4164563.9    24771.2 168.121 <0.0000000000000002 ***
## op_count      52897.5      659.3  80.236 <0.0000000000000002 ***
## arg0           -462.8      896.6  -0.516               0.606    
## arg1           -701.6      922.3  -0.761               0.447    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1399000 on 29996 degrees of freedom
## Multiple R-squared:  0.1767, Adjusted R-squared:  0.1766 
## F-statistic:  2146 on 3 and 29996 DF,  p-value: < 0.00000000000000022
## 
## [1] "GT"   "besu"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1190176  -916936  -756815  1019627  8717342 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 4163368.8    24661.0 168.824 <0.0000000000000002 ***
## op_count      53247.0      672.6  79.163 <0.0000000000000002 ***
## arg0           -371.0      945.2  -0.392               0.695    
## arg1           -136.9      914.9  -0.150               0.881    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1427000 on 29996 degrees of freedom
## Multiple R-squared:  0.1728, Adjusted R-squared:  0.1727 
## F-statistic:  2089 on 3 and 29996 DF,  p-value: < 0.00000000000000022
## 
## [1] "SLT"  "besu"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1341122  -869595  -746707  1091397 11142482 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 3770539.7    24625.2  153.12 <0.0000000000000002 ***
## op_count      67747.9      630.6  107.44 <0.0000000000000002 ***
## arg0          10452.8      852.1   12.27 <0.0000000000000002 ***
## arg1          11181.1      820.8   13.62 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1338000 on 29996 degrees of freedom
## Multiple R-squared:  0.2831, Adjusted R-squared:  0.283 
## F-statistic:  3948 on 3 and 29996 DF,  p-value: < 0.00000000000000022
## 
## [1] "SGT"  "besu"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1324921  -873182  -754144  1102368 15804534 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 3773734.8    23471.4  160.78 <0.0000000000000002 ***
## op_count      66898.9      632.5  105.77 <0.0000000000000002 ***
## arg0          10732.1      915.1   11.73 <0.0000000000000002 ***
## arg1          11409.9      827.9   13.78 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1342000 on 29996 degrees of freedom
## Multiple R-squared:  0.2774, Adjusted R-squared:  0.2773 
## F-statistic:  3838 on 3 and 29996 DF,  p-value: < 0.00000000000000022
## 
## [1] "EQ"   "besu"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1220540  -909152  -753383  1010982 18396553 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 4165610.2    24215.2 172.025 <0.0000000000000002 ***
## op_count      51899.3      668.4  77.646 <0.0000000000000002 ***
## arg0           -270.4      878.8  -0.308               0.758    
## arg1           -603.6      922.8  -0.654               0.513    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1418000 on 29996 degrees of freedom
## Multiple R-squared:  0.1674, Adjusted R-squared:  0.1673 
## F-statistic:  2010 on 3 and 29996 DF,  p-value: < 0.00000000000000022
## 
## [1] "ISZERO" "besu"  
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1059257  -847248  -728291   995444 14083973 
## 
## Coefficients:
##               Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 4142100.02   16809.82 246.410 <0.0000000000000002 ***
## op_count      31369.04     588.40  53.313 <0.0000000000000002 ***
## arg0             70.07     800.18   0.088                0.93    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1248000 on 29997 degrees of freedom
## Multiple R-squared:  0.08655,    Adjusted R-squared:  0.08649 
## F-statistic:  1421 on 2 and 29997 DF,  p-value: < 0.00000000000000022
## 
## [1] "AND"  "besu"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1229432  -937149  -751462   890017 12365489 
## 
## Coefficients:
##               Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 4143966.96   25100.98 165.092 <0.0000000000000002 ***
## op_count      55993.43     702.09  79.752 <0.0000000000000002 ***
## arg0            -81.38     902.59  -0.090               0.928    
## arg1             85.18     981.30   0.087               0.931    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1489000 on 29996 degrees of freedom
## Multiple R-squared:  0.1749, Adjusted R-squared:  0.1749 
## F-statistic:  2120 on 3 and 29996 DF,  p-value: < 0.00000000000000022
## 
## [1] "OR"   "besu"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1228345  -943982  -768150   896989 18286477 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 4173429.1    25835.8 161.537 <0.0000000000000002 ***
## op_count      55139.0      703.1  78.419 <0.0000000000000002 ***
## arg0           -124.6      950.9  -0.131               0.896    
## arg1           -410.1      949.3  -0.432               0.666    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1492000 on 29996 degrees of freedom
## Multiple R-squared:  0.1701, Adjusted R-squared:  0.1701 
## F-statistic:  2050 on 3 and 29996 DF,  p-value: < 0.00000000000000022
## 
## [1] "XOR"  "besu"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1234752  -939170  -755715   878284  9924996 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 4160688.1    25182.9 165.219 <0.0000000000000002 ***
## op_count      55741.3      703.3  79.262 <0.0000000000000002 ***
## arg0           -286.3      907.8  -0.315               0.752    
## arg1           -366.1      917.4  -0.399               0.690    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1492000 on 29996 degrees of freedom
## Multiple R-squared:  0.1732, Adjusted R-squared:  0.1731 
## F-statistic:  2094 on 3 and 29996 DF,  p-value: < 0.00000000000000022
## 
## [1] "NOT"  "besu"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1102030  -863900  -736708  1125299 12082562 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 4147412.8    19068.8 217.498 <0.0000000000000002 ***
## op_count      34886.7      632.9  55.120 <0.0000000000000002 ***
## arg0           -446.4      851.8  -0.524                 0.6    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1343000 on 29997 degrees of freedom
## Multiple R-squared:  0.09198,    Adjusted R-squared:  0.09192 
## F-statistic:  1519 on 2 and 29997 DF,  p-value: < 0.00000000000000022
## 
## [1] "BYTE" "besu"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1208132  -917399  -750738  1088620 10019143 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 4153457.3    25384.7 163.621 <0.0000000000000002 ***
## op_count      52101.6      667.5  78.054 <0.0000000000000002 ***
## arg0          -1634.0      924.3  -1.768              0.0771 .  
## arg1           1428.9      907.3   1.575              0.1153    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1416000 on 29996 degrees of freedom
## Multiple R-squared:  0.169,  Adjusted R-squared:  0.1689 
## F-statistic:  2033 on 3 and 29996 DF,  p-value: < 0.00000000000000022
## 
## [1] "SHL"  "besu"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
##  -962898  -769276  -671789   853774 10908588 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 4248566.8    19016.2  223.42 <0.0000000000000002 ***
## op_count      22002.2      525.7   41.85 <0.0000000000000002 ***
## arg0          -7457.0      683.0  -10.92 <0.0000000000000002 ***
## arg1            447.3      709.9    0.63               0.529    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1115000 on 29996 degrees of freedom
## Multiple R-squared:  0.05872,    Adjusted R-squared:  0.05863 
## F-statistic: 623.8 on 3 and 29996 DF,  p-value: < 0.00000000000000022
## 
## [1] "SHR"  "besu"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1004547  -786369  -672884   841053 10242259 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 4230215.9    18260.6 231.658 <0.0000000000000002 ***
## op_count      23305.9      533.7  43.669 <0.0000000000000002 ***
## arg0          -8424.6      717.5 -11.741 <0.0000000000000002 ***
## arg1           1753.5      705.0   2.487              0.0129 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1132000 on 29996 degrees of freedom
## Multiple R-squared:  0.06392,    Adjusted R-squared:  0.06382 
## F-statistic: 682.7 on 3 and 29996 DF,  p-value: < 0.00000000000000022
## 
## [1] "SAR"  "besu"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -940128 -757434 -687537  851209 7422649 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 4191002.7    20476.1 204.678 < 0.0000000000000002 ***
## op_count      22043.0      522.9  42.156 < 0.0000000000000002 ***
## arg0          -3756.7      722.4  -5.200          0.000000201 ***
## arg1            258.8      713.5   0.363                0.717    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1109000 on 29996 degrees of freedom
## Multiple R-squared:  0.05676,    Adjusted R-squared:  0.05666 
## F-statistic: 601.6 on 3 and 29996 DF,  p-value: < 0.00000000000000022
## 
## [1] "ADDRESS" "besu"   
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
##  -404402  -301396  -168329   153195 14981906 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 1676462.6     4054.4  413.49 <0.0000000000000002 ***
## op_count       9322.9      209.4   44.53 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 444100 on 29998 degrees of freedom
## Multiple R-squared:  0.062,  Adjusted R-squared:  0.06197 
## F-statistic:  1983 on 1 and 29998 DF,  p-value: < 0.00000000000000022
## 
## [1] "ORIGIN" "besu"  
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -402856 -302527 -159081  151402 5851015 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 1676466.3     3986.7  420.52 <0.0000000000000002 ***
## op_count       9405.7      205.9   45.69 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 436700 on 29998 degrees of freedom
## Multiple R-squared:  0.06506,    Adjusted R-squared:  0.06502 
## F-statistic:  2087 on 1 and 29998 DF,  p-value: < 0.00000000000000022
## 
## [1] "CALLER" "besu"  
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -396326 -302028 -175656  153106 3567492 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 1681337.2     3966.6  423.87 <0.0000000000000002 ***
## op_count       9000.2      204.8   43.94 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 434500 on 29998 degrees of freedom
## Multiple R-squared:  0.06047,    Adjusted R-squared:  0.06044 
## F-statistic:  1931 on 1 and 29998 DF,  p-value: < 0.00000000000000022
## 
## [1] "CALLVALUE" "besu"     
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -396220 -297679 -172857  158354 3582012 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 1669785.0     3884.6  429.85 <0.0000000000000002 ***
## op_count       9593.6      200.6   47.83 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 425500 on 29998 degrees of freedom
## Multiple R-squared:  0.07084,    Adjusted R-squared:  0.07081 
## F-statistic:  2287 on 1 and 29998 DF,  p-value: < 0.00000000000000022
## 
## [1] "CALLDATALOAD" "besu"        
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1511021 -1274011 -1205277 -1046574 27989872 
## 
## Coefficients:
##                Estimate  Std. Error t value            Pr(>|t|)    
## (Intercept) 6431117.989   40088.576 160.423 <0.0000000000000002 ***
## op_count      59845.782    1353.789  44.206 <0.0000000000000002 ***
## arg0              3.785       3.450   1.097               0.273    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2872000 on 29997 degrees of freedom
## Multiple R-squared:  0.0612, Adjusted R-squared:  0.06113 
## F-statistic: 977.7 on 2 and 29997 DF,  p-value: < 0.00000000000000022
## 
## [1] "CALLDATASIZE" "besu"        
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -551166 -381336 -257011  302787 3846508 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 1675719.0     4792.6  349.65 <0.0000000000000002 ***
## op_count      16991.9      247.5   68.66 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 525000 on 29998 degrees of freedom
## Multiple R-squared:  0.1358, Adjusted R-squared:  0.1358 
## F-statistic:  4714 on 1 and 29998 DF,  p-value: < 0.00000000000000022
## 
## [1] "CALLDATACOPY" "besu"        
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -15105760  -1936487  -1439525   -659530  49881678 
## 
## Coefficients:
##                   Estimate   Std. Error t value             Pr(>|t|)    
## (Intercept)   4935826.9573  126023.1385  39.166 < 0.0000000000000002 ***
## op_count        -3029.3239    5107.1718  -0.593              0.55308    
## arg0               -3.8052       6.9843  -0.545              0.58587    
## arg1                7.2403       7.2305   1.001              0.31667    
## arg2               30.2297      10.4500   2.893              0.00382 ** 
## op_count:arg2     138.1055       0.5387 256.356 < 0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5647000 on 29994 degrees of freedom
## Multiple R-squared:  0.9194, Adjusted R-squared:  0.9194 
## F-statistic: 6.847e+04 on 5 and 29994 DF,  p-value: < 0.00000000000000022
## 
## [1] "CODESIZE" "besu"    
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -553586 -379600 -252475  297535 6920869 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 1673002.5     4833.3  346.14 <0.0000000000000002 ***
## op_count      17442.7      249.6   69.89 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 529500 on 29998 degrees of freedom
## Multiple R-squared:   0.14,  Adjusted R-squared:   0.14 
## F-statistic:  4884 on 1 and 29998 DF,  p-value: < 0.00000000000000022
## 
## [1] "CODECOPY" "besu"    
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -8496477 -2069935 -1465955  -738496 58298568 
## 
## Coefficients:
##                  Estimate  Std. Error t value             Pr(>|t|)    
## (Intercept)   4762131.797  139197.282  34.211 < 0.0000000000000002 ***
## op_count        -1692.226    5534.577  -0.306              0.75979    
## arg0                1.165       7.219   0.161              0.87181    
## arg1               11.204       7.137   1.570              0.11645    
## arg2               35.165      11.503   3.057              0.00224 ** 
## op_count:arg2     138.024       0.592 233.150 < 0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5818000 on 29994 degrees of freedom
## Multiple R-squared:  0.9105, Adjusted R-squared:  0.9105 
## F-statistic: 6.101e+04 on 5 and 29994 DF,  p-value: < 0.00000000000000022
## 
## [1] "GASPRICE" "besu"    
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -400427 -296037 -164932  157549 3366029 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)  1673171       3834  436.36 <0.0000000000000002 ***
## op_count        9332        198   47.13 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 420000 on 29998 degrees of freedom
## Multiple R-squared:  0.06894,    Adjusted R-squared:  0.06891 
## F-statistic:  2221 on 1 and 29998 DF,  p-value: < 0.00000000000000022
## 
## [1] "RETURNDATASIZE" "besu"          
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -394089 -298509 -180995  155228 5094927 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)  1675706       3893  430.49 <0.0000000000000002 ***
## op_count        9722        201   48.36 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 426400 on 29998 degrees of freedom
## Multiple R-squared:  0.07233,    Adjusted R-squared:  0.0723 
## F-statistic:  2339 on 1 and 29998 DF,  p-value: < 0.00000000000000022
## 
## [1] "RETURNDATACOPY" "besu"          
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -5092469 -2247781 -1798963  -769169 55662894 
## 
## Coefficients:
##                    Estimate    Std. Error t value            Pr(>|t|)    
## (Intercept)   10530020.3647   135860.6034  77.506 <0.0000000000000002 ***
## op_count         10941.6029     5694.5710   1.921              0.0547 .  
## arg0                 6.8092        8.3079   0.820              0.4124    
## arg1                 1.5522        7.6293   0.203              0.8388    
## arg2                16.4646       11.8030   1.395              0.1630    
## op_count:arg2      137.0807        0.6091 225.070 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6253000 on 29994 degrees of freedom
## Multiple R-squared:  0.8997, Adjusted R-squared:  0.8997 
## F-statistic: 5.382e+04 on 5 and 29994 DF,  p-value: < 0.00000000000000022
## 
## [1] "COINBASE" "besu"    
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -400260 -300449 -174975  153341 9524503 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 1679696.7     3972.9  422.79 <0.0000000000000002 ***
## op_count       9094.4      205.2   44.33 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 435200 on 29998 degrees of freedom
## Multiple R-squared:  0.06148,    Adjusted R-squared:  0.06145 
## F-statistic:  1965 on 1 and 29998 DF,  p-value: < 0.00000000000000022
## 
## [1] "TIMESTAMP" "besu"     
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -566886 -381227 -257874  304919 6123200 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 1680439.0     4796.0  350.38 <0.0000000000000002 ***
## op_count      16775.7      247.7   67.73 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 525400 on 29998 degrees of freedom
## Multiple R-squared:  0.1327, Adjusted R-squared:  0.1326 
## F-statistic:  4588 on 1 and 29998 DF,  p-value: < 0.00000000000000022
## 
## [1] "NUMBER" "besu"  
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -542724 -370400 -243923  260081 4163637 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 1682044.1     4695.8   358.2 <0.0000000000000002 ***
## op_count      16343.5      242.5    67.4 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 514400 on 29998 degrees of freedom
## Multiple R-squared:  0.1315, Adjusted R-squared:  0.1315 
## F-statistic:  4543 on 1 and 29998 DF,  p-value: < 0.00000000000000022
## 
## [1] "DIFFICULTY" "besu"      
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -401937 -298581 -165886  155320 3313286 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 1675930.4     3887.3  431.13 <0.0000000000000002 ***
## op_count       9616.6      200.7   47.91 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 425800 on 29998 degrees of freedom
## Multiple R-squared:  0.07107,    Adjusted R-squared:  0.07104 
## F-statistic:  2295 on 1 and 29998 DF,  p-value: < 0.00000000000000022
## 
## [1] "GASLIMIT" "besu"    
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
##  -540265  -369121  -242990   259157 17095537 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 1676121.3     4726.5  354.62 <0.0000000000000002 ***
## op_count      16541.8      244.1   67.77 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 517800 on 29998 degrees of freedom
## Multiple R-squared:  0.1328, Adjusted R-squared:  0.1328 
## F-statistic:  4593 on 1 and 29998 DF,  p-value: < 0.00000000000000022
## 
## [1] "CHAINID" "besu"   
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -395257 -297795 -174832  158706 3167024 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 1676926.9     3849.0  435.68 <0.0000000000000002 ***
## op_count       9332.9      198.8   46.96 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 421600 on 29998 degrees of freedom
## Multiple R-squared:  0.06847,    Adjusted R-squared:  0.06844 
## F-statistic:  2205 on 1 and 29998 DF,  p-value: < 0.00000000000000022
## 
## [1] "SELFBALANCE" "besu"       
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2198770 -1360652  -580952  -139795 22668269 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)  1957245      19700   99.35 <0.0000000000000002 ***
## op_count      438728       1017  431.27 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2158000 on 29998 degrees of freedom
## Multiple R-squared:  0.8611, Adjusted R-squared:  0.8611 
## F-statistic: 1.86e+05 on 1 and 29998 DF,  p-value: < 0.00000000000000022
## 
## [1] "POP"  "besu"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1003736  -571805  -472635   598033  5490854 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 2349846.3    11516.9  204.03 < 0.0000000000000002 ***
## op_count      21192.7      394.8   53.68 < 0.0000000000000002 ***
## arg0           2906.6      516.2    5.63         0.0000000181 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 837500 on 29997 degrees of freedom
## Multiple R-squared:  0.08852,    Adjusted R-squared:  0.08846 
## F-statistic:  1457 on 2 and 29997 DF,  p-value: < 0.00000000000000022
## 
## [1] "MLOAD" "besu" 
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1373617 -1186183 -1129881  -949064 22814158 
## 
## Coefficients:
##                Estimate  Std. Error t value            Pr(>|t|)    
## (Intercept) 6418062.172   38967.570 164.703 <0.0000000000000002 ***
## op_count      24847.635    1284.636  19.342 <0.0000000000000002 ***
## arg0             -1.716       3.309  -0.518               0.604    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2725000 on 29997 degrees of freedom
## Multiple R-squared:  0.01233,    Adjusted R-squared:  0.01226 
## F-statistic: 187.2 on 2 and 29997 DF,  p-value: < 0.00000000000000022
## 
## [1] "MSTORE" "besu"  
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1623071 -1306100 -1121725  -920022 15268993 
## 
## Coefficients:
##                 Estimate   Std. Error t value            Pr(>|t|)    
## (Intercept) 5015816.9300   47421.8266 105.770 <0.0000000000000002 ***
## op_count      33765.0044    1292.8230  26.117 <0.0000000000000002 ***
## arg0              3.2140       3.3543   0.958               0.338    
## arg1              0.5199       3.2830   0.158               0.874    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2742000 on 29996 degrees of freedom
## Multiple R-squared:  0.02226,    Adjusted R-squared:  0.02217 
## F-statistic: 227.7 on 3 and 29996 DF,  p-value: < 0.00000000000000022
## 
## [1] "MSTORE8" "besu"   
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1633584 -1305404 -1219878  -884979 18765166 
## 
## Coefficients:
##                 Estimate   Std. Error t value            Pr(>|t|)    
## (Intercept) 4968599.3224   44472.7843 111.722 <0.0000000000000002 ***
## op_count      24950.9339    1266.4128  19.702 <0.0000000000000002 ***
## arg0              2.9769       3.3735   0.882               0.378    
## arg1             -0.8502       3.3897  -0.251               0.802    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2686000 on 29996 degrees of freedom
## Multiple R-squared:  0.0128, Adjusted R-squared:  0.0127 
## F-statistic: 129.7 on 3 and 29996 DF,  p-value: < 0.00000000000000022
## 
## [1] "JUMP" "besu"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -487288 -309194 -204659  197488 9664457 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 1616946.5     4175.8   387.2 <0.0000000000000002 ***
## op_count      15935.1      215.6    73.9 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 457400 on 29998 degrees of freedom
## Multiple R-squared:  0.154,  Adjusted R-squared:  0.154 
## F-statistic:  5461 on 1 and 29998 DF,  p-value: < 0.00000000000000022
## 
## [1] "JUMPI" "besu" 
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
##  -918714  -754217  -704047   890547 11453028 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 4076167.1    14942.1 272.798 <0.0000000000000002 ***
## op_count      19058.5      528.9  36.037 <0.0000000000000002 ***
## arg0            415.3      682.6   0.608               0.543    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1122000 on 29997 degrees of freedom
## Multiple R-squared:  0.04151,    Adjusted R-squared:  0.04144 
## F-statistic: 649.5 on 2 and 29997 DF,  p-value: < 0.00000000000000022
## 
## [1] "PC"   "besu"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -555390 -378942 -256075  304330 4244186 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 1675541.1     4778.9  350.61 <0.0000000000000002 ***
## op_count      16794.3      246.8   68.05 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 523500 on 29998 degrees of freedom
## Multiple R-squared:  0.1337, Adjusted R-squared:  0.1337 
## F-statistic:  4631 on 1 and 29998 DF,  p-value: < 0.00000000000000022
## 
## [1] "MSIZE" "besu" 
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -396879 -296879 -167778  157907 3669499 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 1673195.4     3844.0  435.27 <0.0000000000000002 ***
## op_count       9665.5      198.5   48.69 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 421100 on 29998 degrees of freedom
## Multiple R-squared:  0.07325,    Adjusted R-squared:  0.07322 
## F-statistic:  2371 on 1 and 29998 DF,  p-value: < 0.00000000000000022
## 
## [1] "GAS"  "besu"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -544857 -371247 -243608  259857 3410851 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 1680527.8     4682.7  358.88 <0.0000000000000002 ***
## op_count      16768.7      241.8   69.34 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 513000 on 29998 degrees of freedom
## Multiple R-squared:  0.1382, Adjusted R-squared:  0.1381 
## F-statistic:  4809 on 1 and 29998 DF,  p-value: < 0.00000000000000022
## 
## [1] "JUMPDEST" "besu"    
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -135366  -62163  -36586   19498 8466349 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 271484.47    1142.13   237.7 <0.0000000000000002 ***
## op_count      7334.28      58.98   124.4 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 125100 on 29998 degrees of freedom
## Multiple R-squared:  0.3401, Adjusted R-squared:  0.3401 
## F-statistic: 1.546e+04 on 1 and 29998 DF,  p-value: < 0.00000000000000022
## 
## [1] "PUSH1" "besu" 
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -486319 -333436 -226197  217285 8778953 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 1669213.4     4285.1  389.54 <0.0000000000000002 ***
## op_count      14408.3      221.3   65.11 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 469400 on 29998 degrees of freedom
## Multiple R-squared:  0.1238, Adjusted R-squared:  0.1238 
## F-statistic:  4240 on 1 and 29998 DF,  p-value: < 0.00000000000000022
## 
## [1] "PUSH2" "besu" 
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -489229 -336228 -226043  212062 4186198 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 1671055.2     4347.6  384.37 <0.0000000000000002 ***
## op_count      14511.6      224.5   64.64 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 476300 on 29998 degrees of freedom
## Multiple R-squared:  0.1222, Adjusted R-squared:  0.1222 
## F-statistic:  4178 on 1 and 29998 DF,  p-value: < 0.00000000000000022
## 
## [1] "PUSH3" "besu" 
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -490551 -339062 -232877  207084 3947042 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 1672445.8     4435.8  377.03 <0.0000000000000002 ***
## op_count      14645.1      229.1   63.93 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 485900 on 29998 degrees of freedom
## Multiple R-squared:  0.1199, Adjusted R-squared:  0.1199 
## F-statistic:  4088 on 1 and 29998 DF,  p-value: < 0.00000000000000022
## 
## [1] "PUSH4" "besu" 
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -491347 -337460 -227770  214512 5894733 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 1673567.3     4325.2  386.94 <0.0000000000000002 ***
## op_count      14413.9      223.4   64.53 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 473800 on 29998 degrees of freedom
## Multiple R-squared:  0.1219, Adjusted R-squared:  0.1219 
## F-statistic:  4165 on 1 and 29998 DF,  p-value: < 0.00000000000000022
## 
## [1] "PUSH5" "besu" 
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -492410 -334672 -225769  215258 6244546 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 1668602.2     4305.7  387.53 <0.0000000000000002 ***
## op_count      14602.5      222.3   65.67 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 471700 on 29998 degrees of freedom
## Multiple R-squared:  0.1257, Adjusted R-squared:  0.1257 
## F-statistic:  4313 on 1 and 29998 DF,  p-value: < 0.00000000000000022
## 
## [1] "PUSH6" "besu" 
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -485549 -336377 -224588  214741 9719174 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 1670748.6     4316.1  387.10 <0.0000000000000002 ***
## op_count      14516.4      222.9   65.13 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 472800 on 29998 degrees of freedom
## Multiple R-squared:  0.1239, Adjusted R-squared:  0.1239 
## F-statistic:  4242 on 1 and 29998 DF,  p-value: < 0.00000000000000022
## 
## [1] "PUSH7" "besu" 
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -486615 -333298 -223939  214198 3213745 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 1665110.9     4270.3  389.93 <0.0000000000000002 ***
## op_count      14672.2      220.5   66.53 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 467800 on 29998 degrees of freedom
## Multiple R-squared:  0.1286, Adjusted R-squared:  0.1286 
## F-statistic:  4427 on 1 and 29998 DF,  p-value: < 0.00000000000000022
## 
## [1] "PUSH8" "besu" 
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -477797 -333077 -229906  215641 3964884 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 1670119.5     4275.7  390.61 <0.0000000000000002 ***
## op_count      14322.8      220.8   64.87 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 468400 on 29998 degrees of freedom
## Multiple R-squared:  0.123,  Adjusted R-squared:  0.123 
## F-statistic:  4208 on 1 and 29998 DF,  p-value: < 0.00000000000000022
## 
## [1] "PUSH9" "besu" 
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -493293 -336326 -226318  213690 3788431 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)  1668933       4338  384.73 <0.0000000000000002 ***
## op_count       14648        224   65.39 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 475200 on 29998 degrees of freedom
## Multiple R-squared:  0.1248, Adjusted R-squared:  0.1247 
## F-statistic:  4276 on 1 and 29998 DF,  p-value: < 0.00000000000000022
## 
## [1] "PUSH10" "besu"  
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -487853 -330064 -225463  220750 3995086 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 1664820.1     4204.4  395.97 <0.0000000000000002 ***
## op_count      14499.0      217.1   66.78 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 460600 on 29998 degrees of freedom
## Multiple R-squared:  0.1294, Adjusted R-squared:  0.1294 
## F-statistic:  4460 on 1 and 29998 DF,  p-value: < 0.00000000000000022
## 
## [1] "PUSH11" "besu"  
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -489353 -333141 -223275  214806 6853663 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 1668486.6     4278.1  390.00 <0.0000000000000002 ***
## op_count      14555.6      220.9   65.89 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 468600 on 29998 degrees of freedom
## Multiple R-squared:  0.1264, Adjusted R-squared:  0.1264 
## F-statistic:  4341 on 1 and 29998 DF,  p-value: < 0.00000000000000022
## 
## [1] "PUSH12" "besu"  
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -484445 -334398 -229075  215912 5081080 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 1669972.6     4324.1  386.20 <0.0000000000000002 ***
## op_count      14426.7      223.3   64.61 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 473700 on 29998 degrees of freedom
## Multiple R-squared:  0.1222, Adjusted R-squared:  0.1221 
## F-statistic:  4174 on 1 and 29998 DF,  p-value: < 0.00000000000000022
## 
## [1] "PUSH13" "besu"  
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -486441 -334095 -226119  215214 4839447 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 1666726.8     4290.0  388.52 <0.0000000000000002 ***
## op_count      14609.5      221.5   65.95 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 469900 on 29998 degrees of freedom
## Multiple R-squared:  0.1266, Adjusted R-squared:  0.1266 
## F-statistic:  4349 on 1 and 29998 DF,  p-value: < 0.00000000000000022
## 
## [1] "PUSH14" "besu"  
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
##  -493186  -337940  -231345   211986 12833420 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 1673542.7     4422.8  378.39 <0.0000000000000002 ***
## op_count      14401.6      228.4   63.06 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 484500 on 29998 degrees of freedom
## Multiple R-squared:  0.117,  Adjusted R-squared:  0.117 
## F-statistic:  3976 on 1 and 29998 DF,  p-value: < 0.00000000000000022
## 
## [1] "PUSH15" "besu"  
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -474266 -331060 -221471  218868 3132757 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 1665789.2     4207.2   395.9 <0.0000000000000002 ***
## op_count      14490.4      217.3    66.7 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 460900 on 29998 degrees of freedom
## Multiple R-squared:  0.1291, Adjusted R-squared:  0.1291 
## F-statistic:  4448 on 1 and 29998 DF,  p-value: < 0.00000000000000022
## 
## [1] "PUSH16" "besu"  
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
##  -478604  -329886  -223806   220815 12877749 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 1665844.2     4249.2  392.04 <0.0000000000000002 ***
## op_count      14323.2      219.4   65.28 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 465500 on 29998 degrees of freedom
## Multiple R-squared:  0.1244, Adjusted R-squared:  0.1243 
## F-statistic:  4261 on 1 and 29998 DF,  p-value: < 0.00000000000000022
## 
## [1] "PUSH17" "besu"  
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -489168 -336430 -225000  213529 8699409 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 1666923.5     4372.7  381.21 <0.0000000000000002 ***
## op_count      14792.1      225.8   65.51 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 479000 on 29998 degrees of freedom
## Multiple R-squared:  0.1251, Adjusted R-squared:  0.1251 
## F-statistic:  4291 on 1 and 29998 DF,  p-value: < 0.00000000000000022
## 
## [1] "PUSH18" "besu"  
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -496639 -333951 -227715  215893 5865073 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)  1668713       4299  388.16 <0.0000000000000002 ***
## op_count       14429        222   64.99 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 470900 on 29998 degrees of freedom
## Multiple R-squared:  0.1234, Adjusted R-squared:  0.1234 
## F-statistic:  4224 on 1 and 29998 DF,  p-value: < 0.00000000000000022
## 
## [1] "PUSH19" "besu"  
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -486716 -332903 -225676  216119 8351796 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 1668245.9     4285.5  389.27 <0.0000000000000002 ***
## op_count      14440.6      221.3   65.25 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 469500 on 29998 degrees of freedom
## Multiple R-squared:  0.1243, Adjusted R-squared:  0.1243 
## F-statistic:  4258 on 1 and 29998 DF,  p-value: < 0.00000000000000022
## 
## [1] "PUSH20" "besu"  
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -490668 -334174 -228436  216451 4488982 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 1668196.9     4287.3  389.10 <0.0000000000000002 ***
## op_count      14535.3      221.4   65.65 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 469700 on 29998 degrees of freedom
## Multiple R-squared:  0.1256, Adjusted R-squared:  0.1256 
## F-statistic:  4310 on 1 and 29998 DF,  p-value: < 0.00000000000000022
## 
## [1] "PUSH21" "besu"  
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -480995 -338361 -231232  212574 5983020 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 1677845.1     4374.6  383.54 <0.0000000000000002 ***
## op_count      14174.2      225.9   62.74 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 479200 on 29998 degrees of freedom
## Multiple R-squared:  0.116,  Adjusted R-squared:  0.116 
## F-statistic:  3937 on 1 and 29998 DF,  p-value: < 0.00000000000000022
## 
## [1] "PUSH22" "besu"  
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -485385 -332610 -227755  213738 3566347 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 1663918.8     4307.9  386.25 <0.0000000000000002 ***
## op_count      14717.8      222.5   66.16 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 471900 on 29998 degrees of freedom
## Multiple R-squared:  0.1273, Adjusted R-squared:  0.1273 
## F-statistic:  4377 on 1 and 29998 DF,  p-value: < 0.00000000000000022
## 
## [1] "PUSH23" "besu"  
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
##  -492810  -339883  -230803   209382 16680952 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 1670897.0     4498.5  371.43 <0.0000000000000002 ***
## op_count      14776.5      232.3   63.61 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 492800 on 29998 degrees of freedom
## Multiple R-squared:  0.1188, Adjusted R-squared:  0.1188 
## F-statistic:  4046 on 1 and 29998 DF,  p-value: < 0.00000000000000022
## 
## [1] "PUSH24" "besu"  
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
##  -481627  -335313  -229977   214480 15524607 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 1670606.6     4401.6  379.55 <0.0000000000000002 ***
## op_count      14498.0      227.3   63.78 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 482200 on 29998 degrees of freedom
## Multiple R-squared:  0.1194, Adjusted R-squared:  0.1194 
## F-statistic:  4068 on 1 and 29998 DF,  p-value: < 0.00000000000000022
## 
## [1] "PUSH25" "besu"  
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
##  -481453  -334899  -225550   216819 15474330 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 1673690.4     4348.4  384.90 <0.0000000000000002 ***
## op_count      14179.5      224.5   63.15 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 476300 on 29998 degrees of freedom
## Multiple R-squared:  0.1173, Adjusted R-squared:  0.1173 
## F-statistic:  3987 on 1 and 29998 DF,  p-value: < 0.00000000000000022
## 
## [1] "PUSH26" "besu"  
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -492537 -338384 -231751  215402 3362459 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 1676569.7     4344.0  385.95 <0.0000000000000002 ***
## op_count      14325.8      224.3   63.86 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 475900 on 29998 degrees of freedom
## Multiple R-squared:  0.1197, Adjusted R-squared:  0.1197 
## F-statistic:  4078 on 1 and 29998 DF,  p-value: < 0.00000000000000022
## 
## [1] "PUSH27" "besu"  
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -488814 -336840 -225554  212808 5811144 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 1668020.2     4354.1   383.1 <0.0000000000000002 ***
## op_count      14816.5      224.8    65.9 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 477000 on 29998 degrees of freedom
## Multiple R-squared:  0.1265, Adjusted R-squared:  0.1264 
## F-statistic:  4342 on 1 and 29998 DF,  p-value: < 0.00000000000000022
## 
## [1] "PUSH28" "besu"  
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -482572 -331603 -223044  217642 3652932 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 1667441.8     4237.3  393.52 <0.0000000000000002 ***
## op_count      14454.2      218.8   66.06 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 464200 on 29998 degrees of freedom
## Multiple R-squared:  0.127,  Adjusted R-squared:  0.127 
## F-statistic:  4364 on 1 and 29998 DF,  p-value: < 0.00000000000000022
## 
## [1] "PUSH29" "besu"  
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -479960 -332906 -225689  220408 8653851 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 1668583.5     4235.4  393.96 <0.0000000000000002 ***
## op_count      14484.7      218.7   66.23 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 464000 on 29998 degrees of freedom
## Multiple R-squared:  0.1276, Adjusted R-squared:  0.1275 
## F-statistic:  4386 on 1 and 29998 DF,  p-value: < 0.00000000000000022
## 
## [1] "PUSH30" "besu"  
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -484838 -334535 -220252  216143 3634176 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)  1668751       4260  391.74 <0.0000000000000002 ***
## op_count       14495        220   65.89 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 466600 on 29998 degrees of freedom
## Multiple R-squared:  0.1264, Adjusted R-squared:  0.1264 
## F-statistic:  4342 on 1 and 29998 DF,  p-value: < 0.00000000000000022
## 
## [1] "PUSH31" "besu"  
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -486562 -334036 -226309  214903 5398817 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 1669356.6     4268.8  391.06 <0.0000000000000002 ***
## op_count      14585.4      220.4   66.17 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 467600 on 29998 degrees of freedom
## Multiple R-squared:  0.1274, Adjusted R-squared:  0.1273 
## F-statistic:  4378 on 1 and 29998 DF,  p-value: < 0.00000000000000022
## 
## [1] "PUSH32" "besu"  
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -481988 -331403 -223603  208787 4045478 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 1663851.3     4263.8  390.23 <0.0000000000000002 ***
## op_count      15042.7      220.2   68.32 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 467100 on 29998 degrees of freedom
## Multiple R-squared:  0.1346, Adjusted R-squared:  0.1346 
## F-statistic:  4668 on 1 and 29998 DF,  p-value: < 0.00000000000000022
## 
## [1] "DUP1" "besu"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -825565 -712726 -666007  799691 6833475 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 4130681.7    14984.7 275.659 <0.0000000000000002 ***
## op_count       9688.8      504.0  19.223 <0.0000000000000002 ***
## arg0           -322.5      670.0  -0.481                0.63    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1069000 on 29997 degrees of freedom
## Multiple R-squared:  0.01218,    Adjusted R-squared:  0.01211 
## F-statistic: 184.9 on 2 and 29997 DF,  p-value: < 0.00000000000000022
## 
## [1] "DUP2" "besu"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
##  -834008  -709853  -665155   803508 14769119 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 4128950.0    14127.0 292.273 <0.0000000000000002 ***
## op_count      10052.7      500.3  20.092 <0.0000000000000002 ***
## arg0           -603.1      635.6  -0.949               0.343    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1061000 on 29997 degrees of freedom
## Multiple R-squared:  0.01331,    Adjusted R-squared:  0.01324 
## F-statistic: 202.3 on 2 and 29997 DF,  p-value: < 0.00000000000000022
## 
## [1] "DUP3" "besu"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -836980 -715944 -670104  791729 6626789 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 4133204.3    15027.4 275.045 <0.0000000000000002 ***
## op_count       9831.8      511.6  19.216 <0.0000000000000002 ***
## arg0           -371.9      702.9  -0.529               0.597    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1085000 on 29997 degrees of freedom
## Multiple R-squared:  0.01217,    Adjusted R-squared:  0.0121 
## F-statistic: 184.8 on 2 and 29997 DF,  p-value: < 0.00000000000000022
## 
## [1] "DUP4" "besu"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -838664 -721166 -674241  791295 9671516 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 4139741.9    14901.1 277.814 <0.0000000000000002 ***
## op_count       9454.3      513.9  18.397 <0.0000000000000002 ***
## arg0           -135.5      673.2  -0.201                0.84    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1090000 on 29997 degrees of freedom
## Multiple R-squared:  0.01116,    Adjusted R-squared:  0.01109 
## F-statistic: 169.2 on 2 and 29997 DF,  p-value: < 0.00000000000000022
## 
## [1] "DUP5" "besu"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -829995 -709774 -663126  803450 6704135 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 4133025.9    14372.8 287.559 <0.0000000000000002 ***
## op_count       9778.5      499.9  19.562 <0.0000000000000002 ***
## arg0           -653.3      637.3  -1.025               0.305    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1060000 on 29997 degrees of freedom
## Multiple R-squared:  0.01263,    Adjusted R-squared:  0.01256 
## F-statistic: 191.9 on 2 and 29997 DF,  p-value: < 0.00000000000000022
## 
## [1] "DUP6" "besu"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -830299 -716662 -670303  798294 6697353 
## 
## Coefficients:
##                Estimate  Std. Error t value            Pr(>|t|)    
## (Intercept) 4123224.517   15419.535 267.403 <0.0000000000000002 ***
## op_count       9911.067     506.761  19.558 <0.0000000000000002 ***
## arg0             -9.266     696.963  -0.013               0.989    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1075000 on 29997 degrees of freedom
## Multiple R-squared:  0.01259,    Adjusted R-squared:  0.01252 
## F-statistic: 191.3 on 2 and 29997 DF,  p-value: < 0.00000000000000022
## 
## [1] "DUP7" "besu"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -837982 -719852 -672416  804249 6875743 
## 
## Coefficients:
##               Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 4159433.69   14972.33 277.808 <0.0000000000000002 ***
## op_count       9576.08     503.28  19.027 <0.0000000000000002 ***
## arg0            -58.49     693.05  -0.084               0.933    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1068000 on 29997 degrees of freedom
## Multiple R-squared:  0.01193,    Adjusted R-squared:  0.01186 
## F-statistic:   181 on 2 and 29997 DF,  p-value: < 0.00000000000000022
## 
## [1] "DUP8" "besu"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -856990 -734178 -686482  801284 7207796 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 4177490.8    14688.5  284.40 <0.0000000000000002 ***
## op_count      10462.5      516.7   20.25 <0.0000000000000002 ***
## arg0            298.7      710.5    0.42               0.674    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1096000 on 29997 degrees of freedom
## Multiple R-squared:  0.01349,    Adjusted R-squared:  0.01343 
## F-statistic: 205.1 on 2 and 29997 DF,  p-value: < 0.00000000000000022
## 
## [1] "DUP9" "besu"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
##  -848533  -720388  -674451   794980 13099809 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 4123184.8    15109.1  272.89 <0.0000000000000002 ***
## op_count      10055.2      513.2   19.59 <0.0000000000000002 ***
## arg0            269.1      708.6    0.38               0.704    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1089000 on 29997 degrees of freedom
## Multiple R-squared:  0.01264,    Adjusted R-squared:  0.01257 
## F-statistic:   192 on 2 and 29997 DF,  p-value: < 0.00000000000000022
## 
## [1] "DUP10" "besu" 
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -825182 -714676 -668533  796119 6301492 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 4115030.7    15084.2 272.805 <0.0000000000000002 ***
## op_count       9994.0      506.8  19.721 <0.0000000000000002 ***
## arg0            514.9      649.8   0.793               0.428    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1075000 on 29997 degrees of freedom
## Multiple R-squared:  0.01282,    Adjusted R-squared:  0.01275 
## F-statistic: 194.8 on 2 and 29997 DF,  p-value: < 0.00000000000000022
## 
## [1] "DUP11" "besu" 
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -867041 -744138 -692709  810204 6845046 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 4231377.9    15352.5 275.615 <0.0000000000000002 ***
## op_count      10258.4      513.0  19.996 <0.0000000000000002 ***
## arg0           -113.2      695.0  -0.163               0.871    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1088000 on 29997 degrees of freedom
## Multiple R-squared:  0.01315,    Adjusted R-squared:  0.01309 
## F-statistic: 199.9 on 2 and 29997 DF,  p-value: < 0.00000000000000022
## 
## [1] "DUP12" "besu" 
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
##  -840531  -721679  -674903   791132 16949621 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 4133274.3    14753.9  280.15 <0.0000000000000002 ***
## op_count       9529.4      514.9   18.51 <0.0000000000000002 ***
## arg0            287.0      666.9    0.43               0.667    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1092000 on 29997 degrees of freedom
## Multiple R-squared:  0.0113, Adjusted R-squared:  0.01123 
## F-statistic: 171.3 on 2 and 29997 DF,  p-value: < 0.00000000000000022
## 
## [1] "DUP13" "besu" 
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
##  -839295  -719532  -672854   803733 11139342 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 4154035.3    15118.2  274.77 <0.0000000000000002 ***
## op_count       9518.3      499.7   19.05 <0.0000000000000002 ***
## arg0            242.5      673.5    0.36               0.719    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1060000 on 29997 degrees of freedom
## Multiple R-squared:  0.01195,    Adjusted R-squared:  0.01189 
## F-statistic: 181.5 on 2 and 29997 DF,  p-value: < 0.00000000000000022
## 
## [1] "DUP14" "besu" 
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
##  -844853  -722887  -675862   803083 16300930 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 4151564.5    14797.4 280.561 <0.0000000000000002 ***
## op_count       9899.6      508.6  19.463 <0.0000000000000002 ***
## arg0            133.8      682.9   0.196               0.845    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1079000 on 29997 degrees of freedom
## Multiple R-squared:  0.01247,    Adjusted R-squared:  0.01241 
## F-statistic: 189.4 on 2 and 29997 DF,  p-value: < 0.00000000000000022
## 
## [1] "DUP15" "besu" 
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -835188 -713275 -666903  799044 9016078 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 4123968.1    15880.3 259.690 <0.0000000000000002 ***
## op_count      10043.3      505.7  19.861 <0.0000000000000002 ***
## arg0           -109.5      721.8  -0.152               0.879    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1073000 on 29997 degrees of freedom
## Multiple R-squared:  0.01298,    Adjusted R-squared:  0.01291 
## F-statistic: 197.2 on 2 and 29997 DF,  p-value: < 0.00000000000000022
## 
## [1] "DUP16" "besu" 
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
##  -907165  -770665  -712961   813818 12491745 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 4318574.4    15729.2 274.557 <0.0000000000000002 ***
## op_count      10540.6      532.2  19.805 <0.0000000000000002 ***
## arg0           -344.4      707.5  -0.487               0.626    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1129000 on 29997 degrees of freedom
## Multiple R-squared:  0.01291,    Adjusted R-squared:  0.01285 
## F-statistic: 196.2 on 2 and 29997 DF,  p-value: < 0.00000000000000022
## 
## [1] "SWAP1" "besu" 
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1009182  -567636  -466119   613531  9825542 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 2332414.6    14478.1 161.100 < 0.0000000000000002 ***
## op_count      25701.6      394.7  65.114 < 0.0000000000000002 ***
## arg0           2581.4      528.7   4.882           0.00000105 ***
## arg1            846.0      514.8   1.644                  0.1    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 837300 on 29996 degrees of freedom
## Multiple R-squared:  0.1245, Adjusted R-squared:  0.1244 
## F-statistic:  1422 on 3 and 29996 DF,  p-value: < 0.00000000000000022
## 
## [1] "SWAP2" "besu" 
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1019310  -579098  -476664   604516  9125621 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 2345771.2    15122.7 155.116 < 0.0000000000000002 ***
## op_count      25594.2      405.7  63.080 < 0.0000000000000002 ***
## arg0            595.5      564.1   1.056                0.291    
## arg1           2611.8      533.8   4.893          0.000000997 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 860700 on 29996 degrees of freedom
## Multiple R-squared:  0.1178, Adjusted R-squared:  0.1177 
## F-statistic:  1335 on 3 and 29996 DF,  p-value: < 0.00000000000000022
## 
## [1] "SWAP3" "besu" 
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -974557 -569853 -477335  604781 7326450 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 2337326.8    14359.9 162.768 < 0.0000000000000002 ***
## op_count      26373.0      397.7  66.309 < 0.0000000000000002 ***
## arg0           1657.4      524.5   3.160              0.00158 ** 
## arg1            668.2      511.6   1.306              0.19149    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 843700 on 29996 degrees of freedom
## Multiple R-squared:  0.1281, Adjusted R-squared:  0.128 
## F-statistic:  1469 on 3 and 29996 DF,  p-value: < 0.00000000000000022
## 
## [1] "SWAP4" "besu" 
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1001165  -579501  -481493   598220  6618157 
## 
## Coefficients:
##               Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 2418452.69   15000.79 161.222 <0.0000000000000002 ***
## op_count      25936.84     405.18  64.013 <0.0000000000000002 ***
## arg0            -78.19     560.58  -0.139               0.889    
## arg1          -1244.96     535.07  -2.327               0.020 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 859500 on 29996 degrees of freedom
## Multiple R-squared:  0.1203, Adjusted R-squared:  0.1202 
## F-statistic:  1368 on 3 and 29996 DF,  p-value: < 0.00000000000000022
## 
## [1] "SWAP5" "besu" 
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
##  -997432  -572869  -482522   608023 13516765 
## 
## Coefficients:
##               Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 2401706.84   15578.99 154.163 <0.0000000000000002 ***
## op_count      25882.88     402.96  64.232 <0.0000000000000002 ***
## arg0             28.68     534.06   0.054               0.957    
## arg1           -439.15     574.54  -0.764               0.445    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 854800 on 29996 degrees of freedom
## Multiple R-squared:  0.1209, Adjusted R-squared:  0.1208 
## F-statistic:  1375 on 3 and 29996 DF,  p-value: < 0.00000000000000022
## 
## [1] "SWAP6" "besu" 
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1002151  -577438  -487761   617588  7810339 
## 
## Coefficients:
##               Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 2433052.74   15651.93 155.447 <0.0000000000000002 ***
## op_count      25882.27     407.39  63.531 <0.0000000000000002 ***
## arg0          -1094.67     547.93  -1.998              0.0457 *  
## arg1             41.46     541.28   0.077              0.9389    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 864200 on 29996 degrees of freedom
## Multiple R-squared:  0.1187, Adjusted R-squared:  0.1186 
## F-statistic:  1347 on 3 and 29996 DF,  p-value: < 0.00000000000000022
## 
## [1] "SWAP7" "besu" 
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1011746  -585524  -492430   631587  7025593 
## 
## Coefficients:
##               Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 2456391.52   15240.01 161.180 <0.0000000000000002 ***
## op_count      25555.38     407.17  62.764 <0.0000000000000002 ***
## arg0           -258.27     564.53  -0.457               0.647    
## arg1            -38.88     580.54  -0.067               0.947    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 863700 on 29996 degrees of freedom
## Multiple R-squared:  0.1161, Adjusted R-squared:  0.116 
## F-statistic:  1313 on 3 and 29996 DF,  p-value: < 0.00000000000000022
## 
## [1] "SWAP8" "besu" 
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -986413 -567670 -474878  608098 5449407 
## 
## Coefficients:
##               Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 2383181.17   14200.63 167.822 <0.0000000000000002 ***
## op_count      26126.48     400.00  65.316 <0.0000000000000002 ***
## arg0            -34.63     530.87  -0.065               0.948    
## arg1            -49.52     542.14  -0.091               0.927    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 848500 on 29996 degrees of freedom
## Multiple R-squared:  0.1245, Adjusted R-squared:  0.1244 
## F-statistic:  1422 on 3 and 29996 DF,  p-value: < 0.00000000000000022
## 
## [1] "SWAP9" "besu" 
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1003402  -578806  -487753   596250 15165622 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 2429339.2    16155.4 150.373 <0.0000000000000002 ***
## op_count      25956.0      411.2  63.129 <0.0000000000000002 ***
## arg0           -211.2      580.2  -0.364              0.7159    
## arg1          -1229.2      551.5  -2.229              0.0258 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 872200 on 29996 degrees of freedom
## Multiple R-squared:  0.1174, Adjusted R-squared:  0.1173 
## F-statistic:  1330 on 3 and 29996 DF,  p-value: < 0.00000000000000022
## 
## [1] "SWAP10" "besu"  
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1027478  -592883  -504995   638784  5225823 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 2476036.2    15932.3 155.410 <0.0000000000000002 ***
## op_count      26931.5      413.5  65.136 <0.0000000000000002 ***
## arg0            407.0      535.3   0.760               0.447    
## arg1          -1072.6      585.6  -1.832               0.067 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 877100 on 29996 degrees of freedom
## Multiple R-squared:  0.124,  Adjusted R-squared:  0.1239 
## F-statistic:  1416 on 3 and 29996 DF,  p-value: < 0.00000000000000022
## 
## [1] "SWAP11" "besu"  
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1026664  -574574  -471696   603114  5348366 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 2396253.6    14372.8 166.722 <0.0000000000000002 ***
## op_count      25281.6      399.2  63.326 <0.0000000000000002 ***
## arg0            895.6      546.9   1.638               0.102    
## arg1           -309.1      542.3  -0.570               0.569    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 846900 on 29996 degrees of freedom
## Multiple R-squared:  0.118,  Adjusted R-squared:  0.1179 
## F-statistic:  1338 on 3 and 29996 DF,  p-value: < 0.00000000000000022
## 
## [1] "SWAP12" "besu"  
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
##  -996408  -581578  -496351   613501 11066225 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 2395428.8    14310.5 167.390 <0.0000000000000002 ***
## op_count      26754.2      411.0  65.101 <0.0000000000000002 ***
## arg0            228.9      531.5   0.431               0.667    
## arg1            409.3      543.4   0.753               0.451    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 871800 on 29996 degrees of freedom
## Multiple R-squared:  0.1238, Adjusted R-squared:  0.1237 
## F-statistic:  1413 on 3 and 29996 DF,  p-value: < 0.00000000000000022
## 
## [1] "SWAP13" "besu"  
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -991913 -576219 -491994  614044 5055159 
## 
## Coefficients:
##               Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 2389699.03   14237.58 167.844 <0.0000000000000002 ***
## op_count      26687.91     405.70  65.782 <0.0000000000000002 ***
## arg0             51.19     540.40   0.095               0.925    
## arg1            675.05     533.02   1.266               0.205    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 860600 on 29996 degrees of freedom
## Multiple R-squared:  0.1261, Adjusted R-squared:  0.126 
## F-statistic:  1443 on 3 and 29996 DF,  p-value: < 0.00000000000000022
## 
## [1] "SWAP14" "besu"  
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -993584 -582117 -481306  596251 5449904 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 2384059.7    14592.2 163.379 <0.0000000000000002 ***
## op_count      25562.5      407.5  62.736 <0.0000000000000002 ***
## arg0           1038.7      570.4   1.821              0.0686 .  
## arg1            461.8      542.8   0.851              0.3949    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 864400 on 29996 degrees of freedom
## Multiple R-squared:  0.1161, Adjusted R-squared:  0.116 
## F-statistic:  1313 on 3 and 29996 DF,  p-value: < 0.00000000000000022
## 
## [1] "SWAP15" "besu"  
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1047306  -602174  -499574   663689  7377915 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 2543547.3    15898.4 159.987 <0.0000000000000002 ***
## op_count      27081.0      422.4  64.114 <0.0000000000000002 ***
## arg0            481.7      562.8   0.856              0.3921    
## arg1          -1299.8      562.7  -2.310              0.0209 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 896000 on 29996 degrees of freedom
## Multiple R-squared:  0.1207, Adjusted R-squared:  0.1206 
## F-statistic:  1372 on 3 and 29996 DF,  p-value: < 0.00000000000000022
## 
## [1] "SWAP16" "besu"  
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1040086  -606805  -501191   639569 12333241 
## 
## Coefficients:
##               Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 2517683.15   15788.52 159.463 <0.0000000000000002 ***
## op_count      25034.85     428.26  58.457 <0.0000000000000002 ***
## arg0             46.51     547.66   0.085               0.932    
## arg1           -191.89     590.75  -0.325               0.745    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 908500 on 29996 degrees of freedom
## Multiple R-squared:  0.1023, Adjusted R-squared:  0.1022 
## F-statistic:  1139 on 3 and 29996 DF,  p-value: < 0.00000000000000022
estimates
##             opcode  env has_significant has_impacting estimate_marginal_ns
## 1              ADD besu           FALSE         FALSE     59139.7381507484
## 2              MUL besu           FALSE         FALSE     90509.6163333146
## 3              SUB besu           FALSE         FALSE     57096.6717899886
## 4              DIV besu            TRUE          TRUE     77384.6505280846
## 5             SDIV besu            TRUE          TRUE     133662.805810687
## 6              MOD besu            TRUE          TRUE     130951.170034169
## 7             SMOD besu            TRUE          TRUE     132826.891880777
## 8           ADDMOD besu            TRUE          TRUE      160454.16620875
## 9           MULMOD besu            TRUE          TRUE     174246.265402108
## 10             EXP besu            TRUE          TRUE     343496.465743394
## 11      SIGNEXTEND besu           FALSE         FALSE     145660.886003304
## 12              LT besu           FALSE         FALSE     52897.4939866554
## 13              GT besu           FALSE         FALSE      53246.989626656
## 14             SLT besu           FALSE         FALSE     67747.9178866525
## 15             SGT besu           FALSE         FALSE     66898.8960433187
## 16              EQ besu           FALSE         FALSE     51899.3274633225
## 17          ISZERO besu           FALSE         FALSE     31369.0441799936
## 18             AND besu           FALSE         FALSE     55993.4300233219
## 19              OR besu           FALSE         FALSE     55139.0193233221
## 20             XOR besu           FALSE         FALSE     55741.3444499885
## 21             NOT besu           FALSE         FALSE     34886.7075633263
## 22            BYTE besu           FALSE         FALSE     52101.6437999896
## 23             SHL besu           FALSE         FALSE     22002.1803233289
## 24             SHR besu           FALSE         FALSE     23305.8617833287
## 25             SAR besu           FALSE         FALSE     22042.9542299955
## 26         ADDRESS besu           FALSE         FALSE     9322.91216666482
## 27          ORIGIN besu           FALSE         FALSE     9405.74188666476
## 28          CALLER besu           FALSE         FALSE     9000.24704666464
## 29       CALLVALUE besu           FALSE         FALSE     9593.64332666484
## 30    CALLDATALOAD besu           FALSE         FALSE     59845.7824299863
## 31    CALLDATASIZE besu           FALSE         FALSE     16991.9485599965
## 32    CALLDATACOPY besu            TRUE          TRUE    -3029.32388348525
## 33        CODESIZE besu           FALSE         FALSE      17442.692686663
## 34        CODECOPY besu            TRUE          TRUE    -1692.22649013348
## 35        GASPRICE besu           FALSE         FALSE     9332.23963333153
## 36  RETURNDATASIZE besu           FALSE         FALSE      9721.5986399978
## 37  RETURNDATACOPY besu            TRUE          TRUE     10941.6029209696
## 38        COINBASE besu           FALSE         FALSE     9094.41983999815
## 39       TIMESTAMP besu           FALSE         FALSE     16775.7228166631
## 40          NUMBER besu           FALSE         FALSE     16343.5492299966
## 41      DIFFICULTY besu           FALSE         FALSE     9616.64865333125
## 42        GASLIMIT besu           FALSE         FALSE     16541.8412599966
## 43         CHAINID besu           FALSE         FALSE     9332.85972999808
## 44     SELFBALANCE besu           FALSE         FALSE     438728.340139909
## 45             POP besu           FALSE         FALSE     21192.6735233291
## 46           MLOAD besu           FALSE         FALSE     24847.6350899939
## 47          MSTORE besu           FALSE         FALSE     33765.0043666591
## 48         MSTORE8 besu           FALSE         FALSE     24950.9338566606
## 49            JUMP besu           FALSE         FALSE     15935.0661899967
## 50           JUMPI besu           FALSE         FALSE     19058.4796699961
## 51              PC besu           FALSE         FALSE     16794.2684833298
## 52           MSIZE besu           FALSE         FALSE     9665.52925999811
## 53             GAS besu           FALSE         FALSE     16768.6736299966
## 54        JUMPDEST besu           FALSE         FALSE     7334.28496333177
## 55           PUSH1 besu           FALSE         FALSE     14408.3057799971
## 56           PUSH2 besu           FALSE         FALSE     14511.5522299971
## 57           PUSH3 besu           FALSE         FALSE     14645.1030233302
## 58           PUSH4 besu           FALSE         FALSE      14413.949889997
## 59           PUSH5 besu           FALSE         FALSE     14602.4568699969
## 60           PUSH6 besu           FALSE         FALSE     14516.4491499969
## 61           PUSH7 besu           FALSE         FALSE      14672.180239997
## 62           PUSH8 besu           FALSE         FALSE     14322.7703966636
## 63           PUSH9 besu           FALSE         FALSE     14648.0053666637
## 64          PUSH10 besu           FALSE         FALSE     14499.0223766635
## 65          PUSH11 besu           FALSE         FALSE     14555.6389199972
## 66          PUSH12 besu           FALSE         FALSE     14426.6753233303
## 67          PUSH13 besu           FALSE         FALSE     14609.5315333301
## 68          PUSH14 besu           FALSE         FALSE      14401.610899997
## 69          PUSH15 besu           FALSE         FALSE      14490.434309997
## 70          PUSH16 besu           FALSE         FALSE     14323.1997133303
## 71          PUSH17 besu           FALSE         FALSE     14792.1068333304
## 72          PUSH18 besu           FALSE         FALSE     14428.7569233302
## 73          PUSH19 besu           FALSE         FALSE      14440.603539997
## 74          PUSH20 besu           FALSE         FALSE     14535.2961233302
## 75          PUSH21 besu           FALSE         FALSE     14174.2461266638
## 76          PUSH22 besu           FALSE         FALSE     14717.7917033303
## 77          PUSH23 besu           FALSE         FALSE      14776.456559997
## 78          PUSH24 besu           FALSE         FALSE     14498.0350066634
## 79          PUSH25 besu           FALSE         FALSE     14179.5140766638
## 80          PUSH26 besu           FALSE         FALSE     14325.8441666638
## 81          PUSH27 besu           FALSE         FALSE     14816.5133666637
## 82          PUSH28 besu           FALSE         FALSE      14454.176679997
## 83          PUSH29 besu           FALSE         FALSE     14484.7470999972
## 84          PUSH30 besu           FALSE         FALSE     14495.0773699971
## 85          PUSH31 besu           FALSE         FALSE     14585.4081999971
## 86          PUSH32 besu           FALSE         FALSE     15042.6825099967
## 87            DUP1 besu           FALSE         FALSE     9688.77807666469
## 88            DUP2 besu           FALSE         FALSE     10052.7201833313
## 89            DUP3 besu           FALSE         FALSE     9831.77762999759
## 90            DUP4 besu           FALSE         FALSE     9454.27888333103
## 91            DUP5 besu           FALSE         FALSE     9778.45543333141
## 92            DUP6 besu           FALSE         FALSE     9911.06687999801
## 93            DUP7 besu           FALSE         FALSE     9576.07521666475
## 94            DUP8 besu           FALSE         FALSE     10462.4805033313
## 95            DUP9 besu           FALSE         FALSE     10055.2243466646
## 96           DUP10 besu           FALSE         FALSE     9993.95662999795
## 97           DUP11 besu           FALSE         FALSE      10258.419089998
## 98           DUP12 besu           FALSE         FALSE     9529.44386666478
## 99           DUP13 besu           FALSE         FALSE     9518.28127999802
## 100          DUP14 besu           FALSE         FALSE     9899.60800666429
## 101          DUP15 besu           FALSE         FALSE     10043.2925666646
## 102          DUP16 besu           FALSE         FALSE     10540.5990233311
## 103          SWAP1 besu           FALSE         FALSE     25701.5755133282
## 104          SWAP2 besu           FALSE         FALSE     25594.2300233282
## 105          SWAP3 besu           FALSE         FALSE     26372.9897199944
## 106          SWAP4 besu           FALSE         FALSE     25936.8432466615
## 107          SWAP5 besu           FALSE         FALSE     25882.8786066616
## 108          SWAP6 besu           FALSE         FALSE     25882.2739866615
## 109          SWAP7 besu           FALSE         FALSE     25555.3819333282
## 110          SWAP8 besu           FALSE         FALSE     26126.4772833284
## 111          SWAP9 besu           FALSE         FALSE     25955.9735666614
## 112         SWAP10 besu           FALSE         FALSE     26931.4635999946
## 113         SWAP11 besu           FALSE         FALSE     25281.5860966614
## 114         SWAP12 besu           FALSE         FALSE     26754.1942333279
## 115         SWAP13 besu           FALSE         FALSE     26687.9064099944
## 116         SWAP14 besu           FALSE         FALSE      25562.452349995
## 117         SWAP15 besu           FALSE         FALSE      27080.950883328
## 118         SWAP16 besu           FALSE         FALSE     25034.8517999951
##     arg0_ns          arg1_ns          arg2_ns     expensive_ns arg0_ns_stderr
## 1      <NA>             <NA>             <NA>             <NA>           <NA>
## 2      <NA>             <NA>             <NA>             <NA>           <NA>
## 3      <NA>             <NA>             <NA>             <NA>           <NA>
## 4      <NA>             <NA>             <NA> 53490.4167445755           <NA>
## 5      <NA>             <NA>             <NA> 61718.8876260387           <NA>
## 6      <NA>             <NA>             <NA> 68190.6096058811           <NA>
## 7      <NA>             <NA>             <NA> 66527.0478967747           <NA>
## 8      <NA>             <NA>             <NA> 105343.306489307           <NA>
## 9      <NA>             <NA>             <NA> 127018.823017713           <NA>
## 10     <NA> 163574.957097498             <NA>             <NA>           <NA>
## 11     <NA>             <NA>             <NA>             <NA>           <NA>
## 12     <NA>             <NA>             <NA>             <NA>           <NA>
## 13     <NA>             <NA>             <NA>             <NA>           <NA>
## 14     <NA>             <NA>             <NA>             <NA>           <NA>
## 15     <NA>             <NA>             <NA>             <NA>           <NA>
## 16     <NA>             <NA>             <NA>             <NA>           <NA>
## 17     <NA>             <NA>             <NA>             <NA>           <NA>
## 18     <NA>             <NA>             <NA>             <NA>           <NA>
## 19     <NA>             <NA>             <NA>             <NA>           <NA>
## 20     <NA>             <NA>             <NA>             <NA>           <NA>
## 21     <NA>             <NA>             <NA>             <NA>           <NA>
## 22     <NA>             <NA>             <NA>             <NA>           <NA>
## 23     <NA>             <NA>             <NA>             <NA>           <NA>
## 24     <NA>             <NA>             <NA>             <NA>           <NA>
## 25     <NA>             <NA>             <NA>             <NA>           <NA>
## 26     <NA>             <NA>             <NA>             <NA>           <NA>
## 27     <NA>             <NA>             <NA>             <NA>           <NA>
## 28     <NA>             <NA>             <NA>             <NA>           <NA>
## 29     <NA>             <NA>             <NA>             <NA>           <NA>
## 30     <NA>             <NA>             <NA>             <NA>           <NA>
## 31     <NA>             <NA>             <NA>             <NA>           <NA>
## 32     <NA>             <NA> 138.105491370495             <NA>           <NA>
## 33     <NA>             <NA>             <NA>             <NA>           <NA>
## 34     <NA>             <NA> 138.023458776764             <NA>           <NA>
## 35     <NA>             <NA>             <NA>             <NA>           <NA>
## 36     <NA>             <NA>             <NA>             <NA>           <NA>
## 37     <NA>             <NA> 137.080651305847             <NA>           <NA>
## 38     <NA>             <NA>             <NA>             <NA>           <NA>
## 39     <NA>             <NA>             <NA>             <NA>           <NA>
## 40     <NA>             <NA>             <NA>             <NA>           <NA>
## 41     <NA>             <NA>             <NA>             <NA>           <NA>
## 42     <NA>             <NA>             <NA>             <NA>           <NA>
## 43     <NA>             <NA>             <NA>             <NA>           <NA>
## 44     <NA>             <NA>             <NA>             <NA>           <NA>
## 45     <NA>             <NA>             <NA>             <NA>           <NA>
## 46     <NA>             <NA>             <NA>             <NA>           <NA>
## 47     <NA>             <NA>             <NA>             <NA>           <NA>
## 48     <NA>             <NA>             <NA>             <NA>           <NA>
## 49     <NA>             <NA>             <NA>             <NA>           <NA>
## 50     <NA>             <NA>             <NA>             <NA>           <NA>
## 51     <NA>             <NA>             <NA>             <NA>           <NA>
## 52     <NA>             <NA>             <NA>             <NA>           <NA>
## 53     <NA>             <NA>             <NA>             <NA>           <NA>
## 54     <NA>             <NA>             <NA>             <NA>           <NA>
## 55     <NA>             <NA>             <NA>             <NA>           <NA>
## 56     <NA>             <NA>             <NA>             <NA>           <NA>
## 57     <NA>             <NA>             <NA>             <NA>           <NA>
## 58     <NA>             <NA>             <NA>             <NA>           <NA>
## 59     <NA>             <NA>             <NA>             <NA>           <NA>
## 60     <NA>             <NA>             <NA>             <NA>           <NA>
## 61     <NA>             <NA>             <NA>             <NA>           <NA>
## 62     <NA>             <NA>             <NA>             <NA>           <NA>
## 63     <NA>             <NA>             <NA>             <NA>           <NA>
## 64     <NA>             <NA>             <NA>             <NA>           <NA>
## 65     <NA>             <NA>             <NA>             <NA>           <NA>
## 66     <NA>             <NA>             <NA>             <NA>           <NA>
## 67     <NA>             <NA>             <NA>             <NA>           <NA>
## 68     <NA>             <NA>             <NA>             <NA>           <NA>
## 69     <NA>             <NA>             <NA>             <NA>           <NA>
## 70     <NA>             <NA>             <NA>             <NA>           <NA>
## 71     <NA>             <NA>             <NA>             <NA>           <NA>
## 72     <NA>             <NA>             <NA>             <NA>           <NA>
## 73     <NA>             <NA>             <NA>             <NA>           <NA>
## 74     <NA>             <NA>             <NA>             <NA>           <NA>
## 75     <NA>             <NA>             <NA>             <NA>           <NA>
## 76     <NA>             <NA>             <NA>             <NA>           <NA>
## 77     <NA>             <NA>             <NA>             <NA>           <NA>
## 78     <NA>             <NA>             <NA>             <NA>           <NA>
## 79     <NA>             <NA>             <NA>             <NA>           <NA>
## 80     <NA>             <NA>             <NA>             <NA>           <NA>
## 81     <NA>             <NA>             <NA>             <NA>           <NA>
## 82     <NA>             <NA>             <NA>             <NA>           <NA>
## 83     <NA>             <NA>             <NA>             <NA>           <NA>
## 84     <NA>             <NA>             <NA>             <NA>           <NA>
## 85     <NA>             <NA>             <NA>             <NA>           <NA>
## 86     <NA>             <NA>             <NA>             <NA>           <NA>
## 87     <NA>             <NA>             <NA>             <NA>           <NA>
## 88     <NA>             <NA>             <NA>             <NA>           <NA>
## 89     <NA>             <NA>             <NA>             <NA>           <NA>
## 90     <NA>             <NA>             <NA>             <NA>           <NA>
## 91     <NA>             <NA>             <NA>             <NA>           <NA>
## 92     <NA>             <NA>             <NA>             <NA>           <NA>
## 93     <NA>             <NA>             <NA>             <NA>           <NA>
## 94     <NA>             <NA>             <NA>             <NA>           <NA>
## 95     <NA>             <NA>             <NA>             <NA>           <NA>
## 96     <NA>             <NA>             <NA>             <NA>           <NA>
## 97     <NA>             <NA>             <NA>             <NA>           <NA>
## 98     <NA>             <NA>             <NA>             <NA>           <NA>
## 99     <NA>             <NA>             <NA>             <NA>           <NA>
## 100    <NA>             <NA>             <NA>             <NA>           <NA>
## 101    <NA>             <NA>             <NA>             <NA>           <NA>
## 102    <NA>             <NA>             <NA>             <NA>           <NA>
## 103    <NA>             <NA>             <NA>             <NA>           <NA>
## 104    <NA>             <NA>             <NA>             <NA>           <NA>
## 105    <NA>             <NA>             <NA>             <NA>           <NA>
## 106    <NA>             <NA>             <NA>             <NA>           <NA>
## 107    <NA>             <NA>             <NA>             <NA>           <NA>
## 108    <NA>             <NA>             <NA>             <NA>           <NA>
## 109    <NA>             <NA>             <NA>             <NA>           <NA>
## 110    <NA>             <NA>             <NA>             <NA>           <NA>
## 111    <NA>             <NA>             <NA>             <NA>           <NA>
## 112    <NA>             <NA>             <NA>             <NA>           <NA>
## 113    <NA>             <NA>             <NA>             <NA>           <NA>
## 114    <NA>             <NA>             <NA>             <NA>           <NA>
## 115    <NA>             <NA>             <NA>             <NA>           <NA>
## 116    <NA>             <NA>             <NA>             <NA>           <NA>
## 117    <NA>             <NA>             <NA>             <NA>           <NA>
## 118    <NA>             <NA>             <NA>             <NA>           <NA>
##       arg1_ns_stderr    arg2_ns_stderr expensive_ns_stderr
## 1               <NA>              <NA>                <NA>
## 2               <NA>              <NA>                <NA>
## 3               <NA>              <NA>                <NA>
## 4               <NA>              <NA>    1276.72900980354
## 5               <NA>              <NA>    1716.26250572823
## 6               <NA>              <NA>    1714.00642195101
## 7               <NA>              <NA>    1696.06287561235
## 8               <NA>              <NA>    1942.88682692474
## 9               <NA>              <NA>    2308.24647927741
## 10  2951.18955494159              <NA>                <NA>
## 11              <NA>              <NA>                <NA>
## 12              <NA>              <NA>                <NA>
## 13              <NA>              <NA>                <NA>
## 14              <NA>              <NA>                <NA>
## 15              <NA>              <NA>                <NA>
## 16              <NA>              <NA>                <NA>
## 17              <NA>              <NA>                <NA>
## 18              <NA>              <NA>                <NA>
## 19              <NA>              <NA>                <NA>
## 20              <NA>              <NA>                <NA>
## 21              <NA>              <NA>                <NA>
## 22              <NA>              <NA>                <NA>
## 23              <NA>              <NA>                <NA>
## 24              <NA>              <NA>                <NA>
## 25              <NA>              <NA>                <NA>
## 26              <NA>              <NA>                <NA>
## 27              <NA>              <NA>                <NA>
## 28              <NA>              <NA>                <NA>
## 29              <NA>              <NA>                <NA>
## 30              <NA>              <NA>                <NA>
## 31              <NA>              <NA>                <NA>
## 32              <NA> 0.538725014664064                <NA>
## 33              <NA>              <NA>                <NA>
## 34              <NA> 0.591992983739053                <NA>
## 35              <NA>              <NA>                <NA>
## 36              <NA>              <NA>                <NA>
## 37              <NA> 0.609056715344308                <NA>
## 38              <NA>              <NA>                <NA>
## 39              <NA>              <NA>                <NA>
## 40              <NA>              <NA>                <NA>
## 41              <NA>              <NA>                <NA>
## 42              <NA>              <NA>                <NA>
## 43              <NA>              <NA>                <NA>
## 44              <NA>              <NA>                <NA>
## 45              <NA>              <NA>                <NA>
## 46              <NA>              <NA>                <NA>
## 47              <NA>              <NA>                <NA>
## 48              <NA>              <NA>                <NA>
## 49              <NA>              <NA>                <NA>
## 50              <NA>              <NA>                <NA>
## 51              <NA>              <NA>                <NA>
## 52              <NA>              <NA>                <NA>
## 53              <NA>              <NA>                <NA>
## 54              <NA>              <NA>                <NA>
## 55              <NA>              <NA>                <NA>
## 56              <NA>              <NA>                <NA>
## 57              <NA>              <NA>                <NA>
## 58              <NA>              <NA>                <NA>
## 59              <NA>              <NA>                <NA>
## 60              <NA>              <NA>                <NA>
## 61              <NA>              <NA>                <NA>
## 62              <NA>              <NA>                <NA>
## 63              <NA>              <NA>                <NA>
## 64              <NA>              <NA>                <NA>
## 65              <NA>              <NA>                <NA>
## 66              <NA>              <NA>                <NA>
## 67              <NA>              <NA>                <NA>
## 68              <NA>              <NA>                <NA>
## 69              <NA>              <NA>                <NA>
## 70              <NA>              <NA>                <NA>
## 71              <NA>              <NA>                <NA>
## 72              <NA>              <NA>                <NA>
## 73              <NA>              <NA>                <NA>
## 74              <NA>              <NA>                <NA>
## 75              <NA>              <NA>                <NA>
## 76              <NA>              <NA>                <NA>
## 77              <NA>              <NA>                <NA>
## 78              <NA>              <NA>                <NA>
## 79              <NA>              <NA>                <NA>
## 80              <NA>              <NA>                <NA>
## 81              <NA>              <NA>                <NA>
## 82              <NA>              <NA>                <NA>
## 83              <NA>              <NA>                <NA>
## 84              <NA>              <NA>                <NA>
## 85              <NA>              <NA>                <NA>
## 86              <NA>              <NA>                <NA>
## 87              <NA>              <NA>                <NA>
## 88              <NA>              <NA>                <NA>
## 89              <NA>              <NA>                <NA>
## 90              <NA>              <NA>                <NA>
## 91              <NA>              <NA>                <NA>
## 92              <NA>              <NA>                <NA>
## 93              <NA>              <NA>                <NA>
## 94              <NA>              <NA>                <NA>
## 95              <NA>              <NA>                <NA>
## 96              <NA>              <NA>                <NA>
## 97              <NA>              <NA>                <NA>
## 98              <NA>              <NA>                <NA>
## 99              <NA>              <NA>                <NA>
## 100             <NA>              <NA>                <NA>
## 101             <NA>              <NA>                <NA>
## 102             <NA>              <NA>                <NA>
## 103             <NA>              <NA>                <NA>
## 104             <NA>              <NA>                <NA>
## 105             <NA>              <NA>                <NA>
## 106             <NA>              <NA>                <NA>
## 107             <NA>              <NA>                <NA>
## 108             <NA>              <NA>                <NA>
## 109             <NA>              <NA>                <NA>
## 110             <NA>              <NA>                <NA>
## 111             <NA>              <NA>                <NA>
## 112             <NA>              <NA>                <NA>
## 113             <NA>              <NA>                <NA>
## 114             <NA>              <NA>                <NA>
## 115             <NA>              <NA>                <NA>
## 116             <NA>              <NA>                <NA>
## 117             <NA>              <NA>                <NA>
## 118             <NA>              <NA>                <NA>
write.csv(estimates, paste0("../../local/", env, "_argument_estimated_cost.csv"), quote=FALSE, row.names=FALSE)